A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM Scientific Reports

It is interesting to notice that topics captured from headlines news are very different from those obtained from the news stories. Thanks to the preparation described earlier, we could build a dedicated LDA model and train our classifier. We tested our model by computing a feature vectors from unseen test data and running a simple logistic regression model to predict whether the next day’s market volatility will increase or decrease, as in Figure 5. Where Nd(neg), Nd(neut), and Nd(pos) denote the daily volume of negative, neutral, and positive tweets.

Both proposed models, leveraging LibreTranslate and Google Translate respectively, exhibit better accuracy and precision, surpassing 84% and 80%, respectively. Compared to XLM-T’s accuracy of 80.25% and mBERT’s 78.25%, these ensemble approaches demonstrably improve sentiment identification capabilities. The Google Translate ensemble model garners what is semantic analysis the highest overall accuracy (86.71%) and precision (80.91%), highlighting its potential for robust sentiment analysis tasks. The consistently lower specificity across all models underscores the shared challenge of accurately distinguishing neutral text from positive or negative sentiment, requiring further exploration and refinement.

Thus, the emotion that increased most in the Spanish pre-covid expansión to covid periods is sadness, followed by fear; that which decreases the most is trust. Coincidences in the greater or lesser expression of emotions in the two periodicals are notable since it provides evidence that the economic atmosphere is similar in the narratives of both periodicals in both periods. With the word limit imposed by EmoLex, the result of the automatic search function is a list of unigrams by frequency with the polarity and emotions marked, as shown in Fig. 3, in which different colours have been assigned to make identification easier. According to Plamper and Lazier (2001, pp. 134–135), the decade between 1990 and 2000 was an era of optimism on the part of investors, but this dissipated with the bursting of the dot.com bubble, and confidence only began to build again from 2003 onwards. This study, indeed, seeks to illustrate how fear and greed as expressions of emotions occur verbally in the news of the two periods considered.

2. Aggregating news and sentiment scores

On another note, with the popularity of generative text models and LLMs, some open-source versions could help assemble an interesting future comparison. Moreover, the capacity of LLMs such as ChatGPT to explain their decisions is an outstanding, arguably unexpected accomplishment that can revolutionize the field. As seen in the table below, achieving such a performance required lots of financial and human resources. In the case of this sentence, ChatGPT did not comprehend that, although striking a record deal may generally be good, the SEC is a regulatory body. Hence, striking a record deal with the SEC means that Barclays and Credit Suisse had to pay a record value in fines. I always intended to do a more micro investigation by taking examples where ChatGPT was inaccurate and comparing it to the Domain-Specific Model.

Whereas in the pre-COVID period, 64% of the words were positive, during the COVID period there was a relative balance (76 positive vs. 82 negative words, 48% vs. 51%). It seems that the Spanish Newspaper Expansión does not want to create alarm among its readership, and this leads to the use of positive and negative lexis in roughly equal proportions. The English periodical is negative in both periods, as we have noted, but significant variations are seen between the pre-COVID and COVID periods, with a notable increase in negative (from 151 to 306) and positive (from 42 to 102) items in the second. It should be borne in mind that the emotional activity in both periodicals is ‘very intense’ in both periods. An initial analysis with a million-word sample per sub-corpus was made with Lingmotif 2, for the reasons explained above.

Table of Contents

Therefore, the effect of danmaku sentiment analysis methods based on sentiment lexicon isn’t satisfactory. The state-of-the-art performance of SLSA has been achieved by various DNN models. In \(S_0\), the first part expresses a positive polarity, but the polarity of the second part is negative. In \(S_1\), the BERT model fails to detect the positive polarity of the combination of “not” and “long”. The implementation of ABSA is fraught with challenges that stem from the complexity and nuances of human language27,28.

• You’ll notice that our two tables have one thing in common (the documents / articles) and all three of them have one thing in common — the topics, or some representation of them.
• Evaluating the numbers in these matrices helps understand the models’ overall performance and effectiveness in sentiment analysis tasks.
• It is noteworthy that by choosing document-level granularity in our analysis, we assume that every review only carries a reviewer’s opinion on a single product (e.g., a movie or a TV show).
• On the other hand, collocations are two or more words that often go together.

Berners-Lee started describing something like the Semantic Web in the earliest days of his work on the World Wide Web starting in 1989. At the time, he was developing sophisticated applications for creating, editing and viewing connected data. But these all required expensive NeXT workstations, and the software was not ready for mass consumption. Consumers often fill out dozens of forms containing the same information, such as name, ChatGPT App address, Social Security number and preferences with dozens of different companies. To address these problems, Berners-Lee’s company, Inrupt, is working with various communities, hospitals and governments to roll out secured data pods built on the Solid Open Source protocol that allows consumers to share access to their data. Learning platforms, job websites and HR teams may all use different terms to describe job skills.

What Is Semantic Analysis? Definition, Examples, and Applications in 2022

Yeshiwas and Abebe8 adopted a deep learning approach for Amharic sentiment analysis, annotating 1600 comments with seven classes. Using CNN and various experiments, they achieved accuracy rates ranging from 40 to 90.1%. These findings laid the foundation for future exploration of Amharic sentiment analysis. Turegn19 evaluated the impact of data preprocessing on Amharic sentiment analysis, integrating emojis, and comparing human and automatic annotation. The study found that stemming had no positive impact, emojis provided a negligible improvement, and automatic annotation overlapped significantly with human annotation.

The above deep transfer model is utilized to realize the customer requirements classification among functional domain, behavioral domain and structural domain in the customer requirement descriptions of elevator by fine-tuning training. Moreover, the ILDA is adopted to mine the functional customer requirements that can represent customer intention maximally. Finally, an effective accuracy of customer requirements classification is acquired by using the BERT deep transfer model. Meanwhile, five kinds of customer ChatGPT requirements of elevator and corresponding keywords as well as their weight coefficients in the topic-word distribution are extracted. This work can provide a novel research perspective on customer requirements mining for product conceptual design through natural language processing. Kapočiūtė-Dzikienė et al.29, claim that deep learning models tend to underperform when used for morphologically rich languages and hence recommend traditional machine learning approach with manual feature engineering.

SAP HANA Sentiment Analysis

Taking the neologism “蚌埠住了” as an example, after the binary neologism “蚌埠” is counted, the mutual information between “蚌埠” and “住” is calculated by shifting to the right and finally expanding to “蚌埠住了”. By calculating the mutual information and eliminating the words with low branch entropy and removing the first and last deactivated words, the new word set is obtained after eliminating the existing old words. In addition, this method achieves dynamic evolution of the danmaku lexicon by excluding new words that may contain dummy words at the beginning and end, and adding new words to the lexicon without repetition after comparing them with those in the danmaku lexicon. This approach improves the quality of word splitting and solves the problems of unrecognized new words, repetitions, and garbage strings.

You can foun additiona information about ai customer service and artificial intelligence and NLP. In the rest of this post, I will qualitatively analyze a couple of reviews from the high complexity group to support my claim that sentiment analysis is a complicated intellectual task, even for the human brain. Although for both the high sentiment complexity group and the low subjectivity group, the S3 does not necessarily fall around the decision boundary, yet -for different reasons- it is harder for our model to predict their sentiment correctly. Traditional classification models cannot differentiate between these two groups, but our approach provides this extra information. The following two interactive plots let you explore the reviews by hovering over them. 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.

Social media platforms provide valuable insights into public attitudes, particularly on war-related issues, aiding in conflict resolution efforts18. Despite their precision and time-consuming nature, machine-learning algorithms are the foundation of sentiment analysis16. We assessed whether topics derived from financial news and social media may provide accuracy in predicting market volatility.

The class with the highest class probabilities is taken to be the predicted class. The id2label attribute which we stored in the model’s configuration earlier on can be used to map the class id (0-4) to the class labels (1 star, 2 stars..). As you can see in the above screenshot, Google does not allow the negative sentiment expressed in the search query to influence it into showing a web page with a negative sentiment. Earlier that year Danny published an official Google announcement about featured snippets where he mentioned sentiment. But the context of sentiment was that for some queries there may be a diversity of opinions and because of that Google might show two featured snippets, one positive and one negative. The evidence and facts are out there to show where Google’s research has been focusing in terms of sentiment analysis.

A sentiment analysis tool uses artificial intelligence (AI) to analyze textual data and pick up on the emotions people are expressing, like joy, frustration or disappointment. Decoding those emotions and understanding how customers truly feel about your brand is what sentiment analysis is all about. The feature vector for an interval is a topic-count sparse vector, it represents the number of times each topic appears in headlines/tweets or articles within the given interval. The target vector is then constructed by pairing binary direction labels from market volatility data to each feature vector.

These values help determine which stories should be considered news and the significance of these stories in news reporting. However, different news organizations and journalists may emphasize different news values based on their specific objectives and audience. Consequently, a media outlet may be very keen on reporting events about specific topics while turning a blind eye to others. For example, news coverage often ignores women-related events and issues with the implicit assumption that they are less critical than men-related contents (Haraldsson and Wängnerud, 2019; Lühiste and Banducci, 2016; Ross and Carter, 2011).

Although not often thought of as a semantic SEO strategy, structured data is all about directly conveying the meaning of content to Google crawlers. According to a recent study of 2.5 million search queries, Google’s “People also ask” feature now shows up for 48.4% of all search queries, and often above position 1. Another way to improve the semantic depth of your content is to answer the common questions that users are asking in relation to your primary keyword. Instead, the best way to increase the length of your web content is to be more specific, nuanced, and in-depth with the information you’re providing users about the primary topic. Keyword clustering is all about leveraging Google’s strong semantic capabilities to improve the total number of keywords our content ranks for.

Particularly, the thinking aloud of subjects and experiment materials are all in Chinese in order to reduce the cognitive load of subjects. The dimension of hidden layer is 768 and there are 12 attention heads in total. The dimension of hidden layer is 768 and there are 16 attention heads in total. The input Chinese sentences are converted into word vectors including token, position and segment, which respectively represent the word itself, word position and sentence dependency. The obtained vector representations are input into the BERT model, and the bi-directional Transformer structure can effectively extract semantic associations in the text data.