REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research Scientific Reports

However, none of these reports employed a paradigm strategically designed to elicit action concepts in unfolding speech, let alone while exploring their sensitivity to distinct disease phenotypes. A fruitful path for clinical PD research thus emerges at the crossing of behavioral neurology, cognitive neuroscience, and natural language processing. In our study, we investigated the spatio-temporal dynamics of visual word processing both in terms of neural activation, frequency and directionality of information flow. You can foun additiona information about ai customer service and artificial intelligence and NLP. In EEG connectivity studies, spurious connectivity can occur due to the spatial spread (resulting from volume conduction) during which signals coming from different neural sources are mixed before reaching the scalp surface. Thus, connectivity measured on this surface could reveal artificial or spurious connections which do not result from true neuronal interactions47.

Through a granular analysis of the dimensions of consumer confidence, we found that the extent to which the news impacts consumers’ economic perception changes if we consider people’s current versus prospective judgments. Our forecasting results demonstrate that the SBS indicator predicts most consumer perception categories more than the language sentiment expressed in the articles. ERKs seem to impact more the Personal climate, i.e., consumers’ perception of their current ability to save, purchase durable assets, and feel economically stable. In addition, we find a disconnect between the ERKs’ impact on the current and future assessments of the economy, which is aligned with other studies68,69. While the Consumer Confidence Index has often been considered a suitable predictor of economic growth and a good indicator of consumers’ optimism about the current economy, short-term estimations may show deviations from long-term trends, likely caused by nonsystematic shocks. First, we want to know whether there is variation in the evolutionary dynamics of different meanings.

This means the generation of phonology would be earlier than the P2 effect might suggest. The speed at which semantics is accessed by words with consistent (simple) and inconsistent (difficult) spelling–sound correspondences can be used to test predictions of models of reading aloud. Dual-route models that use a word-form lexicon predict consistent words may access semantics before inconsistent words. It predicts inconsistent words may access semantics before consistent words, at least for some readers. We tested this by examining event-related potentials in a semantic priming task using consistent and inconsistent target words with either unrelated/related or unrelated/nonword primes. The unrelated/related primes elicited an early effect of priming on the N1 with consistent words.

Data availibility

We thank the Fields Institute for financial support and facilitating the collaborative research project. We thank Gemma Boleda for discussion and feedback, Yiwei Luo and Aparna Balagopalan for sharing resources. Results of directionality inference from generalized linear mixed modeling with language as a random effect. These technologies not only help to optimise the email channel but also have applications in the entire digital communication such as content summarisation, smart database, etc. And most probably, more use cases will appear and reinvent the customer-bank relationship soon.

For example, if negative sentiment increases after a new product release, that could be an early indication that something is going wrong, enabling the company to do a deep dive to understand which features are causing problems or to get more agents on board to handle problems. TruncatedSVD will return it to as a numpy array of shape (num_documents, num_components), so we’ll turn it into a Pandas dataframe for ease of manipulation. Repeat the steps above for the test set as well, but only using transform, not fit_transform. The values in 𝚺 represent how much each latent concept explains the variance in our data. When these are multiplied by the u column vector for that latent concept, it will effectively weigh that vector.

If you’re not familiar with a confusion matrix, as a rule of thumb, we want to maximise the numbers down the diagonal and minimise them everywhere else. This article assumes some understanding of basic NLP preprocessing and of word vectorisation (specifically tf-idf vectorisation). Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction techniques that follows the same method as Singular Value Decomposition. LSA ultimately reformulates text data in terms of r latent (i.e. hidden) features, where r is less than m, the number of terms in the data.

Our sequential Generator model had three Dense layers with two layers activated by the “ReLU” activation function. We maintained the dimensions of the output layer like the dimensions of the dataset. The first two layers were activated by the “ReLU” activation semantics analysis function, and the output layer was activated by the “Sigmoid” activation function to discriminate the real (True or 1) and synthetic (False or 0) data. We compiled the Discriminator model with optimizer as “ADAM” and loss function as “binary_crossentropy”.

There may be dual motivations for semantic change to take place in regular ways from the perspectives of speaker and listener. From a speaker’s view, regular semantic change might facilitate the grounding or structuring of new meaning given existing words (Srinivasan et al., 2019), and hence easing the process of creating and learning meaning change. From a listener’s view, regular meaning change might facilitate the interpretation or construal of novel meaning, provided the speaker and listener have some shared knowledge about the world and the situation (Clark and Clark, 1979; Traugott and Dasher, 2001). Importantly, we believe that regularity may be manifested and understood in different aspects in the context of semantic change across languages.

The value range of Lin Similarity is divided into 9 subintervals, and the number of texts in CT and CO that fall into each subinterval is counted. This figure provides a clearer illustration of the nuanced differences between the Lin Similarity distributions of CT and CO than a boxplot. The value range of Wu-Palmer Similarity is divided into 10 subintervals, and the number of texts in CT and CO that fall into each subinterval is counted. This figure provides a clearer illustration of the nuanced differences between the Wu-Palmer Similarity distributions of CT and CO than a boxplot. The other major effect lies in the conversion and addition of certain semantic roles for logical explicitation.

Bibliometric analysis of willingness to communicate in the English as a second language (ESL) context

At the lab, the owners were instructed to say words for objects before showing their dog either the correct item or a different one. For example, an owner might say “Look, here’s the ball”, but hold up a Frisbee instead. The experiments were repeated multiple times with matching and non-matching objects. More direct evidence for canine cognitive prowess came in 2011 when psychologists in South Carolina reported that after three years of intensive training, a border collie called Chaser had learned the names of more than 1,000 objects, including 800 cloth toys, 116 balls and 26 Frisbees. Dogs understand what certain words stand for, according to researchers who monitored the brain activity of willing pooches while they were shown balls, slippers, leashes and other highlights of the domestic canine world.

Colleges can employ support group intervention, which has positive impacts on social support among college students (Lamothe et al., 1995; Mattanah et al., 2012). As King and Hicks (2021) implied, self-acceptance may contribute to increasing meaning in life and some related literature are listed below. Longitudinal findings showed that college students who feel unoriented from their true selves, indicating a lower level of self-acceptance, tend ChatGPT App to be devoid of academic motivation, perceiving all efforts as meaningless and showing a low level of meaning in life (Kim et al., 2018). Moreover, self-acceptance was found to share a robust relation with increased positive feelings and life satisfaction, which is identified as the promoter of meaning in life (Miao and Gan, 2019; Liu F. et al., 2021). Indeed, it can be inferred that self-acceptance could be closely related to meaning in life.

A visual and intuitive form builder is available, or forms can be imported as XLS files. In the case of health research, semantic annotation can help describe the data that is being collected. It can be helpful to extract and link different research datasets described using the same vocabulary.

Correlations between tasks

That functional nature of activity remains unchanged in TT–hence the same field of exploring in TT. Here is a case of compressing types of process, but it involves only a shift within a single transitivity process, the relational one. We collected all the ACPP and their translations in the three Governance volumes as research materials, to address the research questions. The first volume (Xi, 2014a) explored President Xi’s critical works, including the speeches, talks, interviews, instructions, and correspondence from November 15, 2012, to June 13, 2014.

In the initial testing, each formula was executed in tandem, and the equations would be used to compare the effect of variation in the parameters. For purposes of consistency, and to distinguish from previous terminology, new symbols will be used for the components necessary for these comparisons. The symbol \(\alpha\) designates the initial search or seed term, the basis of all comparisons for these formulas. The symbol \(\tau\) will refer to a token contained within a processed tweet, where \(\tau _i\) indicates one of many such tokens in any given tweet.

The TWT differs from these cases in that it does not directly require inference or reasoning. The reaction time of making meaningfulness judgments in tasks similar to TWT is around 1 s for humans19,32, which suggests that it is a qualitatively different process than those used in typical benchmark tasks such as puzzle solving or logical and commonsense reasoning. A limitation in breaking down a complex chain of reasoning into smaller problems should not affect performance on the TWT. Understanding these phrases requires understanding the constituent concepts, and then using world knowledge to determine whether the combination makes sense in some manner.

We found a robust difference in the semantics of how female journalists wrote about the reform, relative to male journalists, and that female journalists contributed to media coverage at a higher-than-expected rate. The tendency for media coverage to be written with a non-neutral sentiment can be understood in terms of the enduring political tensions over gender equality, the role of the EU and families’ rights to self-organization. That female journalists over-contributed to media coverage is interesting in understanding topic assignments or interest in parental leave.

Meaning patterns of the construction under investigation also accord with the theory of prototypicality. Prototypicality originally means the degree of category membership (Goldberg, 1995), and in this research, it means the identified meaning patterns are the most typical ones or the most representative of the meaning denoted by the NP de VP construction. Meaning patterns of the NP slot and the VP slot are confirmed by referring to the semantic features of these NPs and VPs. Those lexical items with statistically significant association strengths represent core members of the NP de VP construction. The meanings of these core members in turn represent the prototypical meaning of this construction. Core members of NPs in this construction briefly include zhidu “regulation”, tixi “system”, yewu “business”, etc., and thus the typical meaning patterns are “regulations”, “systems” and “business” (cf. Fig. 2).

Among these, explicitation stands out to be the most semantically salient hypothesis. It was first formulated by Blum-Kulka (1986) to suggest that translated texts have a higher level of cohesive explicitness. Baker (1996) broadened its definition into the “translator’s tendency to explicate information that is implicit in the source text”, emphasizing that explicitation in translated texts is not limited to cohesion, but can also be observed at the informational level.

It is not surprising that average consumers have a better understanding of their personal situation when responding to questions but may be less informed about economic cycles. When answering questions about their own financial situation, individuals are likely to have a more accurate understanding of their personal circumstances. However, when it comes to broader economic trends and cycles, the average consumer may not have the same level of knowledge or expertise. This is understandable, as economic cycles can be complex and difficult to understand without specialized training or experience.

According to the table, it can be observed that in the combination of template and data consistency, the GEV of SCZ patients reached 88.5%, while the GEV of HC was 85.5%. This indicates that the two microstate templates can effectively express the corresponding original brain topographic. However, in inconsistent combinations, the average GEV of the HS sequence was only 74.4%, while the average GEV of the SH sequence was 78.9%. This means that inconsistent combinations exhibit significantly lower quality while expressing the original data. Other quality evaluation indicators have also produced similar results, emphasizing that the combination of template and data consistency exhibits higher quality in constructing microstate sequences.

All three domains including tubules (cyan), sheets (yellow) and SBTs (magenta) were precisely classified by ERnet (Fig. 2d). A 3D rendering of ERnet segmented structures demonstrates attachment of tubules to sheets. A 3D reconstruction of SIM image sections validated that SBTs are directly attached to sheets and are not the result of a projection view artifact. All three domains including tubules (cyan), sheets (yellow) and SBTs (magenta) were precisely classified by ERnet (Fig. 2e).

In a similar vein, meanings of core members of VPs are summarized as such meaning patterns as “implementation”, “achievement”, and “establishment” (cf. Fig. 1). This paper proceeds further than Shen and Wang’s (2002) study in at least two respects. On the one hand, we considered not only lexical items that could enter the NP slot of the NP de VP construction but also the ones that are representative of the typical meanings of the NPs. By so doing, it will further facilitate our understanding of the typical meaning that the NP de VP construction could denote.

Finally, the User Support module is a supporting tool to facilitate communication between research teams (often located in distinct research centers) and the project’s coordination staff (Fig. 8). This tool allows users to send specific requests regarding the data stored in the REDCap database, such as unlocking records for editing and data deletions. The Processor receives the data collected in KoBoToolbox as a JSON object, parsed to remove unnecessary elements unrelated to the data of interest. After verifying the authentication credentials, the metadata is queried to obtain the URL and the token of the REDCap API (from redcap_projects) and to verify if it is the first form in the project (from redcap_forms).

In each case, we take the source probability ratio of a pair of senses in Equation (4) to be proportional to the ratio of their values under the predictor variables in question. One can train machines to make near-accurate ChatGPT predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation.

These citations constitute only a small part of the whole political work and some are only a part of a complex sentence; therefore, they often inevitably bear the transitivity characteristics of political texts, more or less. “尚贤者” is an embedded identifying-relational clause that functions as a participant in the whole sentence “尚贤者, 政之本也” (respecting the virtuous is the root of governance). In the English translation, “尚贤者” was reproduced as a nominal group “exaltation of the virtuous”, instead of rendering it as an embedded clause with a verbal group to be consistent with the original structure. The experiential meaning realized in two clauses in the ST now is condensed into one clause in English, stressing the most important process based on the translators’ understanding. The tendency of the compression of process types can change the intensiveness of meaning realized in clauses. As Chinese relies much on verbs and verbal groups to construe the experience of the world, clauses of different kinds tend to occur in which more information is embedded, causing the high intensity of meaning expressed in one sentence.

Change in overall representational similarity structure

We then normalized these shift values by dividing each by half of their range across all shifts. Since we discarded all shifts where either source or target did not have an available value for concreteness, frequency, or valence, we analyzed a reduced set of 859 semantic change pairs. Alternative methods of assigning values to senses that retain more data points can be found in Supplementary material, and our results hold robustly in that more exhaustive dataset. Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context. Albeit extensive studies of translation universals at lexical and grammatical levels, there has been scant research at the syntactic-semantic level.

The singular value not only weights the sum but orders it, since the values are arranged in descending order, so that the first singular value is always the highest one. The REDbox framework is constantly evolving to meet the target audience’s needs, taking into account the dynamism and multidisciplinarity of the health research area. As future work progresses and as the software matures, specific comments from key users will be collected to guide the evolution of each module. Although the TB scenario motivated the solution, it applies to other health fields as well. This work has presented REDbox, a comprehensive framework for integrated data collection and management in tuberculosis research. The use of REDCap and KoBoToolbox together has allowed the transparent combination of the advantages of each, helping researchers manage and maintain data while increasing the satisfaction of the final users responsible for collecting data in the field.

After manually processing those overlapping lexical items in the NP slot of the NP de VP construction, we finally confirmed 62 types of nouns. Considering their covarying collexemes in the VP slot of the construction, a 62 (types of nouns in the NP slot) × 99 (types of covarying collexemes in the VP slot) contingency table is subsequently formatted. Running the function hclust in R language yields a cluster dendrogram presented in Fig. This observation further supports our hypothesis that the trends in semantic change direction with regards to concreteness, frequency, and valence of the source and target words are shared and not language-specific. Semantic analysis is a method used in linguistics, computer science, and artificial intelligence to understand the meaning of words and sentences in context. It examines relationships among words and phrases to comprehend the ideas and concepts they convey.

Additionally, collaborative relationships or citation connections among the sampled countries might inform new narratives. Another future direction that would be vital to expanding our field would be considering why the research impact of these 13 countries differed and, furthermore, what determines their impact within ‘language and linguistics’ research. The last analyses about impactful topics have also shed light on another possible research direction. The results of Tables 5–7 substantiated that the research interest in computerized language analyses has intensified among the Asian ‘language and linguistics’ community. Conversely, the findings of ‘language and linguistics’ research are becoming critical ingredients in cutting-edge Computer Science technologies (Clark et al., 2012; Haddi et al., 2013; Rodriguez et al., 2012).

• The semantic role labelling tools used for Chinese and English texts are respectively, Language Technology Platform (N-LTP) (Che et al., 2021) and AllenNLP (Gardner et al., 2018).
• “Bringing Cambridge Semantics to Altair’s broad customer base through the Altair Units business model–and integrating it into Altair RapidMiner–is an exciting prospect for us and for our customers,” Pieper said in a press release.
• This leads to an idiosyncratic information structure in the target language and hence, the deviation between the translated and target languages.
• The model predicted histologic features match what in expected in both normal and pancreatitis samples.

In relation to lexical items in the NP slot of the construction, Shen and Wang (2000) argued that NPs that denote a sense of prominence (i.e., high informativity and/or high accessibility), could enter the NP slot of the construction. Their argument is further corroborated by the findings of this study which has identified such meaning patterns as “internal traits”, “medical names”, “regulations”, “results”, “systems”, and “business”. According to Shen and Wang (2002, p. 30), specific nouns are more accessible than abstract nouns.

Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority. By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience.

Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning Scientific Reports

Its scalability and speed optimization stand out, making it suitable for complex tasks. Natural language processing (NLP) is a field within artificial intelligence that enables computers to interpret and understand human language. Using machine learning and AI, NLP tools analyze text or speech to identify context, meaning, and patterns, allowing computers to process language much like humans do. One of the key benefits of NLP is that it enables users to engage with computer systems through regular, conversational language—meaning no advanced computing or coding knowledge is needed.

The actual sentiment labels of reviews are shown by green (positive) and red (negative). It is evident from the plot that most mislabeling happens close to the decision boundary as expected. To solve this issue, I suppose that the similarity of a single word to a document equals the average of its similarity to the top_n most similar words of the text. Then I will calculate this similarity for every word in my positive and negative sets and average over to get the positive and negative scores. To put it differently, to estimate the positive score for a review, I calculate the similarity of every word in the positive set with all the words in the review, and keep the top_n highest scores for each positive word and then average over all the kept scores. Released to the public by Stanford University, this dataset is a collection of 50,000 reviews from IMDB that contains an even number of positive and negative reviews with no more than 30 reviews per movie.

The overall polarity of the review was computed by summing the polarity scores of all words in the review and dividing by their distance from the aspect term. If a sentence’s polarity score is less than zero (0), it is classified as negative; if the score is equal to zero, it is defined as neutral; and if the score is equal to or more than one, it is defined as positive. These classified features and n-gram features have been used to train machine learning algorithms.

Stochastic gradient descent (SGD) and K-nearest neighbour (KNN) and had performed, followed by LR, which has 66.7% and 63.6% of accuracy. For the second model, the dataset consists of 65 instances with the label ‘Physical’ and 43 instances with the label ‘Non-physical. The feature engineering technique, the Term Frequency/ Inverse Document Frequency (TFIDF) is applied. After that, the Principal Component Analysis (PCA) is applied for dimensionality reduction. The 108 instances are then split into train dataset and test dataset, where 30% of the dataset is used for testing the performance of the model.

They build several machine learning classifiers and deep learning classifiers using the neural network LSTM and GRU. Some machine classification technique was introduced and tabulated in Table 1. Rocchio classification uses the frequency of the words from a vector and compares the similarity of that vector and a predefined prototype vector. This classification is not general because it is limited to retrieving a few relevant documents.

LR algorithms achieve the highest accuracy out of all others machine learning and deep learning algorithms. In the cited paper, sentiment analysis of Arabic text ChatGPT App was performed using pre-trained word embeddings. Recently, pre-trained algorithms have shown the state of the art results on NLP-related tasks27,28,29,30.

Step 3. Use the best social media sentiment analysis tool

Rules are established on a comment level with individual words given a positive or negative score. If the total number of positive words exceeds negative words, the text might be given a positive sentiment and vice versa. You then use sentiment analysis tools to determine how customers feel about your products or services, customer service, and advertisements, for example. Sentiment analysis, or opinion mining, analyzes qualitative customer feedback (often written language) to determine whether it contains positive, negative, or neutral emotions about a given subject. And, since sentiment is often shared through online platforms like ecommerce sites, social media, and digital accounts, you can use those channels to access a deeper, almost intuitive understanding of customer desires and behaviors. The next step involves combining the predictions furnished by the BERT, RoBERTa, and GPT-3 models through a process known as majority voting.

The numbers in the table represent the forecasting error of each model with respect to the AR(2) forecasting error. We used the Diebold-Mariano test66 to determine if the forecasting errors of each model were statistically worse (in italic) than the best model, whose RMSFEs are highlighted in bold. The Granger causality tests for sentiment indicate significance only for the second question, which pertains to the assessment semantic analysis of text of the household’s economic situation. In line with the findings presented in Table 2, it appears that ERKs have a greater influence on current assessments than on future projections. This is aligned with the current debate in the literature on consumer confidence, as it is still unclear whether surveys merely reflect current or past events or provide useful information about the future of household spending8.

The hybrid architectures avail from the outstanding characteristic of each network type to empower the model. This section explains the details of the proposed set of machine learning, rule-based, a set of deep learning algorithms and proposed mBERT model. The set of machine learning algorithms such as KNN, RF, NB, LR, MLP, SVM, and AdaBoost are used to classify Urdu reviews.

A distribution on topics is first sampled from a Dirichlet distribution, and a topic is further chosen based on this distribution. Moreover, each document is modeled as a distribution over topics, and a topic is represented as a distribution over words. Another challenge when translating foreign language text for sentiment analysis is the idiomatic expressions and other language-specific attributes that may elude accurate capture by translation tools or human translators43. You can foun additiona information about ai customer service and artificial intelligence and NLP. One of the primary challenges encountered in foreign language sentiment analysis is accuracy in the translation process. Machine translation systems often fail to capture the intricate nuances of the target language, resulting in erroneous translations that subsequently affect the precision of sentiment analysis outcomes39,40.

“Performance evaluation of topic modeling algorithms for text classification,” in rd International Conference on Trends in Electronics and Informatics (ICOEI) (Tirunelveli). • R TM packages include three packages that are capable of doing topic modeling analysis which are MALLET, topic models, and LDA. Also, the R language has many packages and libraries for effective topic modeling like LSA, LSAfun (Wild, 2015), topicmodels (Chang, 2015), and textmineR (Thomas Jones, 2019).

In terms of syntactic subsumption, it seems that CT have an inclination for simplification in argument structure. Moreover, the average number of argument structures in Chinese sentences should be bigger than that in English sentences since they have a similar average number of semantic roles in a sentence. The distinctive aspect of our textual entailment analysis is that we take a given sentence as H and create its T by changing the predicate in the sentence into its root hypernym. In this way we manually create a determined entailment relationship between T and H. Based on this methodology, the extra information I(E) in Formula (1) can be approximated by the distance between the original predicate and its root hypernym. Then the distance can be quantified as 1 minus the Wu-Palmer Similarity or Lin Similarity between the original predicate and its root hypernym.

Study 1

Specifically, the authors used a pre-trained multilingual transformer model to translate non-English tweets into English. They then used these translated tweets as additional training data for the sentiment analysis model. This simple technique allows for taking advantage of multilingual models for non-English tweet datasets of limited size. Sentiment analysis, the computational task of determining the emotional tone within a text, has evolved as a critical subfield of natural language processing (NLP) over the past decades1,2. It systematically analyzes textual content to determine whether it conveys positive, negative, or neutral sentiments. This capability holds immense importance in understanding public opinion, customer feedback, and social discourse, making it a fundamental principle in various applications across fields such as marketing, politics, and customer service3,4,5.

Moreover, researchers have sought to supplement their findings by examining evidence from alternative sources such as literary texts and life writings. Consequently, the task of extracting specific content from extensive texts like novels is arduous and time-consuming. The scholarly community has made substantial progress in comprehending the multifaceted nature of sexual harassment cases in the Middle East (Karami et al., 2021).

• In addition, the Bi-GRU-CNN trained on the hyprid dataset identified 76% of the BRAD test set.
• For instance, we may use consumer surveys in conjunction with our methods to gain a more comprehensive understanding of the market.
• The Continuous Skip-gram model uses training data to predict the context words based on the target word’s embedding.
• This section will guide you through four steps to conduct a thorough social sentiment analysis, helping you transform raw data into actionable strategies.
• In the above example, the translation follows the information structure of the source text and retains the long attribute instead of dividing it into another clause structure.

Through a granular analysis of the dimensions of consumer confidence, we found that the extent to which the news impacts consumers’ economic perception changes if we consider people’s current versus prospective judgments. Our forecasting results demonstrate that the SBS indicator predicts most consumer perception categories more than the language sentiment expressed in the articles. ERKs seem to impact more the Personal climate, i.e., consumers’ perception of their current ability to save, purchase durable assets, and feel economically stable. In addition, we find a disconnect between the ERKs’ impact on the current and future assessments of the economy, which is aligned with other studies68,69.

The bias of machine learning models stems from the data preparation phase, where a rule-based algorithm is employed to identify instances of sexual harassment. The accuracy of this process heavily relies on the collection of sexual harassment words used to detect such sentences, thereby influencing the final outcome. Consequently, it becomes imperative to incorporate manual interpretation in order to review and validate the selection of sexual harassment sentences. However, it is important to acknowledge that both manual annotation and computational modelling introduce systematic errors that can lead to bias. To mitigate these defects, a few domain experts should be involved in the manual interpretation process to ensure a more reliable result.

Examples of Semantic Analysis

Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans. Thus, as and when a new change is introduced on the Uber app, the semantic analysis algorithms start listening to social network feeds to understand whether users are happy about the update or if it needs further refinement. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority.

Subsequently, data preparation, modelling, evaluation, and visualization phases were conducted for each model in order to assess their performance. 1 and provides an overview of the entire process, from data pre-processing to visualization. Furthermore, this framework can be used as a reference for future studies on sexual harassment classification. There are three types of procedures, which are supervised method, lexicon-based method, and semantic based method.

Another critical consideration in translating foreign language text for sentiment analysis pertains to the influence of cultural variations on sentiment expression. Diverse cultures exhibit distinct conventions in conveying positive or negative emotions, posing challenges for accurate sentiment capture by translation tools or human translators41,42. The work described in12 focuses on scrutinizing the preservation of sentiment through machine translation processes. To this end, a sentiment gold standard corpus featuring annotations from native financial experts was curated in English. The first objective was to assess the overall translation quality using the BLEU algorithm as a benchmark. The second experiment identified which machine translation engines most effectively preserved sentiments.

Bi-LSTM, in contrast to LSTM, contains forward and backward layers for conducting additional feature extractions which is suitable for Amharic language because the language by its nature needs context information to understand the sentence. One copy of the hidden layer fits in the input sequences as the traditional LSTM, while the other is placed on a reversed copy of the input sequence. For both the forward and backward hidden layers in our model, the researcher used a bidirectional LSTM with a 64-memory unit. Then add a dropout of (0.4, 0.5), Random state of 50, Embedded size of 32, batch size of 100, and 3 epochs to minimize overfitting. To calculate the loss function Binary Classification were used and Adam as an optimizer.

An RNN network was trained using feature vectors computed using word weights and other features as percentage of positive, negative and neutral words. RNN, SVM, and L2 Logistic Regression classifiers were tested and compared using six datasets. In addition, LSTM models were widely applied for Arabic SA using word features and applying shallow structures composed of one or two layers15,40,41,42, as shown in Table 1. Meltwater’s latest sentiment analysis model incorporates features such as attention mechanisms, sentence-based embeddings, sentiment override, and more robust reporting tools. With these upgraded features, you can access the highest accuracy scores in the field of natural language processing. However, with advancements in linguistic theory, machine learning, and NLP techniques, especially the availability of large-scale training corpora (Shao et al., 2012), SRL tools have developed rapidly to suit technical and operational requirements.

By adapting Plutchik’s taxonomy (Fig. 1), we can fit the emotions to the Fear and Greed Index, and thus grade them in terms of their relation to being more or less prone to risk aversion or risk attraction. Incidentally, rational choice theory in economics, as the best-established theory on investment behaviour, considers that individuals react predictably and rationally in terms of economic or financial decisions (Zey, 1998). We can also group by the entity types to get a sense of what types of entites occur most in our news corpus. Besides these four major categories of parts of speech , there are other categories that occur frequently in the English language. These include pronouns, prepositions, interjections, conjunctions, determiners, and many others.

Word embeddings have become integral to tasks such as text classification, sentiment analysis, machine translation and more. BERT is a pre-trained language model that has been shown to be very effective for a variety of NLP tasks, including sentiment analysis. BERT is a deep learning model that is trained on a massive dataset of text and code. This training allows BERT to learn the contextual relationships between words and phrases, which is essential for accurate sentiment analysis. Emotion-based sentiment analysis goes beyond positive or negative emotions, interpreting emotions like anger, joy, sadness, etc. Machine and deep learning algorithms usually use lexicons (a list of words or phrases) to detect emotions.

It utilizes natural language processing techniques such as topic clustering, NER, and sentiment reporting. Companies use the startup’s solution to discover anomalies and monitor key trends from customer data. The architecture of RNNs allows previous outputs to be used as inputs, which is beneficial when using sequential data such as text.

Furthermore, each POS tag like the noun (N) can be further subdivided into categories like singular nouns (NN), singular proper nouns (NNP), and plural nouns (NNS). You can see that the semantics of the words are not affected by this, yet our text is still standardized. Lemmatization is very similar to stemming, where we remove word affixes to get to the base form of a word. However, the base form in this case is known as the root word, but not the root stem.

• To identify the most suitable models for predicting sexual harassment types in this context, various machine learning techniques were employed.
• To calculate the loss function Binary Classification were used and Adam as an optimizer.
• Word stems are also known as the base form of a word, and we can create new words by attaching affixes to them in a process known as inflection.
• Furthermore, Sawhney et al. introduced the PHASE model166, which learns the chronological emotional progression of a user by a new time-sensitive emotion LSTM and also Hyperbolic Graph Convolution Networks167.

Their extensive testing indicates that this model sets a new benchmark, surpassing previous state-of-the-art methods52,53. This study presents two models that have been developed to address the issue of sexual harassment. The first model is a machine learning model which is capable of accurately classifying different types of sexual harassment. The second model, which leverages a deep learning approach, is used to classify sentiment and emotion. To ensure the accuracy of the models, a comprehensive text pre-processing process was applied to the text data.

In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main ChatGPT branch located a’’, ‘stays at,’ and others connect the above entities. Users can download pre-trained GloVe embeddings and fine-tune them for specific applications or use them directly.

If your company doesn’t have the budget or team to set up your own sentiment analysis solution, third-party tools like Idiomatic provide pre-trained models you can tweak to match your data. These graphical representations serve as a valuable resource for understanding how different combinations of translators and sentiment analyzer models influence sentiment analysis performance. Following the presentation of the overall experimental results, the language-specific experimental findings are delineated and discussed in detail below.

CNN predicts 1904 correctly identified positive comments in sentiment analysis and 2707 correctly identified positive comments in offensive language identification. Logistic regression is a classification technique and it is far more straightforward to apply than other approaches, specifically in the area of machine learning. The character vocabulary includes all characters found in the dataset (Arabic characters, , Arabic numbers, English characters, English numbers, emoji, emoticons, and special symbols). CNN, LSTM, GRU, Bi-LSTM, and Bi-GRU layers are trained on CUDA11 and CUDNN10 for acceleration. Combinations of word embedding and handcrafted features were investigated for sarcastic text categorization54. Sarcasm was identified using topic supported word embedding (LDA2Vec) and evaluated against multiple word embedding such as GloVe, Word2vec, and FastText.

Trump plans to dismantle Biden AI safeguards after victory

Wipro will collaborate with clients to develop industry-specific solutions that can be integrated into existing business workflows across sectors including retail, healthcare, financial services, manufacturing, and telecommunications. The experience zone will serve as a hands-on demonstration space where enterprises can experiment with Google’s AI technologies, including Gemini models and Vertex AI, to identify suitable generative AI applications for their businesses. Companies like K2K, a smart city application provider in the Nvidia Metropolis ecosystem, will use the new Nvidia AI Blueprint to build AI agents that analyze live traffic cameras in real time. This will enable city officials to ask questions about street activity and receive recommendations on ways to improve operations. The company also is working with city traffic managers in Palermo, Italy, to deploy visual AI agents using NIM microservices and Nvidia AI Blueprints. To support their work, a new Nvidia AI Blueprint for video search and summarization will enable developers in virtually any industry to build visual AI agents that analyze video and image content.

Surround yourself with people who are willing to push boundaries, challenge conventional thinking, and foster a culture where experimentation with AI is encouraged and celebrated. At the same time, encourage a mindset of continuous learning and innovation within your organization. Start by taking a close look at your day-to-day operations to identify any manual processes that consume valuable time.

AI Semiconductor Performance With Wafer Technology

Sure, there have been some glaringly obvious uses to AI-generated images in scientific papers (there was that ridiculous image of rat with a huge penis, complete with nonsense AI-generated text). Having forward-thinking individuals like Kleinhandler—who can see the big picture and think outside the box—is crucial for driving this kind of innovation. These leaders recognize AI’s potential and know how to strategically implement it to align with business goals. Early Wednesday morning, Donald Trump became the presumptive winner of the 2024 US presidential election, setting the stage for dramatic changes to federal AI policy when he takes office early next year. Among them, Trump has stated he plans to dismantle President Biden’s AI Executive Order from October 2023 immediately upon taking office.

If you are already a registered user of TheHindu Businessline and logged in, you may continue to engage with our articles. Users can access their older comments by logging into their accounts on Vuukle. Global professional services company Accenture has integrated Nvidia AI Blueprints into its Accenture AI Refinery, which is built on Nvidia AI Foundry and enables customers to develop custom AI models trained on enterprise data. But with a different AI environment these days in the wake of ChatGPT and media-reality-warping image synthesis models, those earlier orders don’t likely point the way to future positions on the topic. That means you can expect AI-generated videos to get way better, very soon, and do away with the telltale artifacts — flaws or inaccuracies — like morphing faces and shape-shifting objects that mark current AI creations.

What to look out for: Imposter videos vs. text-to-image videos

The key to identifying AI-generated videos (or any AI modality), then, lies in AI literacy. “Understanding that [AI technologies] are growing and having that core idea of ‘something I’m seeing could be generated by AI,’ is more important than, say, individual cues,” said Lyu, who is the director of UB’s Media Forensic Lab. Given how endless the internet is, there are creeps lurking around every corner. Catfishers can easily target teenagers and younger individuals, making these guardrails essential.

Once you have a clear picture, reach out to someone knowledgeable in AI for insights on how these tasks might be automated. In a warehouse environment, an AI agent built with this workflow could alert workers if safety protocols are breached. At busy intersections, an AI agent could identify traffic collisions and generate reports to aid emergency response efforts. And in the field of public infrastructure, maintenance workers could ask AI agents to review aerial footage and identify degrading roads, train tracks or bridges to support proactive maintenance.

These agents can answer user questions, generate summaries and enable alerts for specific scenarios. The innovative model, developed at Georgia Tech, creates invisible digital masks for personal photos to thwart unwanted online facial recognition while preserving the image quality. Right now, AI-generated videos are still a relatively nascent modality compared to AI-generated text, images, and audio, because getting all the details right is a challenge that requires a lot of high quality data. “But there’s no fundamental obstacle to getting higher quality data,” only labor-intensive work, said Siwei Lyu, a professor of computer science and engineering at University at Buffalo SUNY. The researchers said a notable visual difference often exists between the original and photos using current masking models. However, Chameleon preserves much of the original photo’s quality among various facial images.

Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. By leveraging these technologies, you can uncover patterns that may not be immediately apparent, and you can refine your strategies accordingly. Sometimes, I find myself getting carried away with a particular belief, convinced that I’ve identified the best solution to a problem. It’s not until I’m challenged, often by my friends in entrepreneurial communities, that I realize there might be a better way to tackle the issue.

Meta also talked about a previous option for teenagers that let their friends vouch for them. This AI-powered sensor has applications across industries, serving food production, quality assurance, and health diagnostics by delivering quick, non-invasive assessments of product quality. By assessing the data holistically, the neural network adjusts to sample variations, much like how humans perceive differences in food. “We would like to use these techniques to protect images from being used to train artificial intelligence generative models. We could protect the image information from being used without consent,” he said. Victor Morales, VP of Global System Integrators Partnerships at Google Cloud, highlighted generative AI’s potential in solving complex industry challenges.

Nvidia AI Blueprint makes it easy for any devs to build automated agents that analyze video

These tools can identify trends and provide actionable insights, such as which content types drive the most engagement or which demographics are most likely to convert. For example, a connection of mine, David Kleinhandler came out of retirement to create Optifino, an innovative insurtech platform backed by top Silicon Valley investors. Optifino combines cutting-edge AI with financial and life insurance planning to streamline portfolio management for insurance customers. Their tools also make it easier for advisors to identify the right insurance products for their clients, based on their tax- and legacy-related goals. Putting an AI engine to work has cut down on onerous work, and helped clients save money while being better set up for long-term performance. The facility will offer businesses access to Google Gemini’s Large Language Models for testing AI-driven use cases such as natural language understanding, image generation, and predictive analytics.

Due to this, those under 18 years of age can easily add years and join the less-restricted side of Instagram. Bloomberg reports that Meta is working on utilizing an AI-powered adult classifier tool to identify such users and stop them dead in their tracks. John is an adviser for the growth marketing agency Relevance, a company that helps brands be the most relevant in their industry.

It’s your best defense against being duped by AI deepfakes, disinformation, or just low-quality junk. It’s a hard skill to develop, because every aspect of the online world fights against it in a bid for your attention. But the good news is, it’s possible to fine-tune your AI detection instincts. Well, turns out, they “may” be able to appeal and get rid of the teen account status. However, according to a Meta representative, that’s under development right now.

The Rise of AI Across All Industries

Kamali says to look for “sociocultural implausibilities” or context clues where the reality of the situation doesn’t seem plausible. “You don’t immediately see the telltales, but you feel that something is off — like an image of Biden and Obama wearing pink suits,” or the Pope in a Balenciaga puffer jacket. Farid also said to look out for “temporal inconsistencies,” such as “the building added a story, or the car changed colors, things that are physically not ChatGPT App possible,” he said. “And often it’s away from the center of attention that where that’s happening.” So, hone in on the background details. You might see unnaturally smooth or warped objects, or a person’s size change as they walk around a building, said Lyu. Meanwhile, in other generative AI news, people are roasting Netflix’s plan to make games with AI after it laid off its human developers, and Transport for Ireland’s AI Halloween art has spooked commuters.

Deployed on Nvidia GPUs at the edge, on premises or in the cloud, it can vastly accelerate the process of combing through video archives to identify key moments. Enterprises and public sector organizations around the world are developing AI agents to boost the capabilities of workforces that rely on visual information from a growing number of devices — including cameras, IoT sensors and vehicles. Accenture, Dell ChatGPT and Lenovo are among the companies tapping a new Nvidia AI Blueprint to develop visual AI agents that can boost productivity, optimize processes and create safer spaces. Trump supporters in the US government have criticized the measures, as TechCrunch points out. In March, Representative Nancy Mace (R-S.C.) warned that reporting requirements could discourage innovation and prevent developments like ChatGPT.

Among its core provisions, the order established the US AI Safety Institute (AISI) and lays out requirements for companies to submit reports about AI training methodologies and security measures, including vulnerability testing data. The order also directed the Commerce Department’s National Institute of Standards and Technology (NIST) to develop guidance to help companies identify and fix flaws in their AI models. Lyu’s 2018 paper about detecting AI-generated ai picture identifier videos because the subjects didn’t blink properly was widely publicized in the AI community. As a result, people started looking for eye-blinking defects, but as the technology progressed, so did more natural blinks. “People started to think if there’s a good eye blinking, it must not be a deepfake and that’s the danger,” said Lyu. You can foun additiona information about ai customer service and artificial intelligence and NLP. “We actually want to raise awareness but not latch on particular artifacts, because the artifacts are going to be amended.”.

Unlike current models, which produce different masks for each user’s photos, Chameleon creates a single, personalized privacy protection (P-3) mask for all of a user’s facial photos. Anyone who posts photos of themselves risks having their privacy violated by unauthorized facial image collection. Online criminals and other bad actors collect facial images by web scraping to create databases.

• You might even be inspired to innovate within your field, developing something entirely new that transforms how your business operates.
• Meta also talked about a previous option for teenagers that let their friends vouch for them.
• The research showcases the potential for this AI-powered sensor technology to expand into diverse applications.
• After introducing teen accounts and even giving Instagram new powers to combat sextortion, Meta has now shifted its focus to eradicating the problem at its very root.
• That said, there are some things to look for if you suspect an AI video deepfake.

Science journal is already using the tool, although it says it has not yet detected an AI images. Meanwhile, Springer Nature, which publishes the journal Nature, is developing its own detection tools for both text and images, called Geppetto and SnapShot. Proofig’s AI Image Fabrication detector has been trained on a vast dataset of known generative-AI images. We’ve reported a lot about the risks of using AI image generators for branding, which can cause a fierce public backlash. We might still be able to tell when an image is AI if it shows a human or another familiar subject, but it can be more difficult when it comes to images of microscopic phenomena.

He’s also the co-founder of Calendar, a scheduling and time management app. It’s transforming industries by streamlining operations, improving decision-making, and unlocking new opportunities for growth. The key is to start small, identify areas of improvement, and integrate AI thoughtfully.

• Science journal is already using the tool, although it says it has not yet detected an AI images.
• Educate your team about the potential of AI, and create an environment where experimentation is encouraged.
• Unlike current models, which produce different masks for each user’s photos, Chameleon creates a single, personalized privacy protection (P-3) mask for all of a user’s facial photos.
• Harnessing AI tools for data accuracy and insights can significantly elevate your decision-making processes.

And Senator Ted Cruz (R-Texas) characterized NIST’s AI safety standards as an attempt to control speech through “woke” safety requirements. If you suspect a lip sync, focus your attention on the subject’s mouth — especially the teeth. With fakes, “We have seen people who have irregularly shaped teeth,” or the number of teeth change throughout the video, said Lyu. Another strange sign to look out for is “wobbling of the lower half” of the face, said Lyu. “There’s a technical procedure where you have to exactly match that person’s face,” he said. “As I’m talking, I’m moving my face a lot, and that alignment, if you got just a little bit of imprecision there, human eyes are able to tell.” This gives the bottom half of the face a more liquid, rubbery effect.