Sentiment analysis becomes an essential part of every social network, as it enables decision-makers to know more about users' opinions in almost all life aspects. Despite its importance, there are multiple issues it encounters like the sentiment of the sarcastic text which is one of the main challenges of sentiment analysis. This paper tackles this challenge by introducing a novel system (SAIDS) that predicts the sentiment, sarcasm and dialect of Arabic tweets. SAIDS uses its prediction of sarcasm and dialect as known information to predict the sentiment. It uses MARBERT as a language model to generate sentence embedding, then passes it to the sarcasm and dialect models, and then the outputs of the three models are concatenated and passed to the sentiment analysis model. Multiple system design setups were experimented with and reported. SAIDS was applied to the ArSarcasm-v2 dataset where it outperforms the state-of-the-art model for the sentiment analysis task. By training all tasks together, SAIDS achieves results of 75.98 FPN, 59.09 F1-score and 71.13 F1-score for sentiment analysis, sarcasm detection, and dialect identification respectively. The system design can be used to enhance the performance of any task which is dependent on other tasks.

A simple guide to fine-tuning a Transformers DistilBert Model using Tensorflow on Apple M1 Chip for a Sentiment Analysis Task. During the execution of the model.fit() After investigation, I found this solution that works for TF2.6 and forces the GPU as the only device available to run the network Read the CSV file and apply a lambda function to convert labels from text to numbers. Label positive is 1 and label negative is 0. The dataset will be split into training, validation, and testing, according to the percentages of 70, 15, and 15. For this copy-paste tutorial, the distilbert-base-uncased has been used, so the DistilBertTokenizerFast is used to tokenize the dataset, the output is in numpy form.

To the best of our knowledge, this paper made the first attempt to answer whether word segmentation is necessary for Vietnamese sentiment classification. To do this, we presented five pre-trained monolingual S4- based language models for Vietnamese, including one model without word segmentation, and four models using RDRsegmenter, uitnlp, pyvi, or underthesea toolkits in the pre-processing data phase. According to comprehensive experimental results on two corpora, including the VLSP2016-SA corpus of technical article reviews from the news and social media and the UIT-VSFC corpus of the educational survey, we have two suggestions. Firstly, using traditional classifiers like Naive Bayes or Support Vector Machines, word segmentation maybe not be necessary for the Vietnamese sentiment classification corpus, which comes from the social domain. Secondly, word segmentation is necessary for Vietnamese sentiment classification when word segmentation is used before using the BPE method and feeding into the deep learning model. In this way, the RDRsegmenter is the stable toolkit for word segmentation among the uitnlp, pyvi, and underthesea toolkits.

Flipkart is one of the most popular Indian companies. It is an e-commerce platform that competes with popular e-commerce platforms like Amazon. One of the most popular use cases of data science is the task of sentiment analysis of product reviews sold on e-commerce platforms. So, if you want to learn how to analyze the sentiment of Flipkart reviews, this article is for you. In this article, I will walk you through the task of Flipkart reviews sentiment analysis using Python.

ByteDance, the Chinese parent company of popular video app TikTok, said Thursday that some employees improperly accessed the TikTok user data of two journalists and were no longer employed by the company, an email seen by Reuters shows. ByteDance employees accessed the data as part of an unsuccessful effort to investigate leaks of company information earlier this year, and were aiming to identify potential connections between two journalists, a former BuzzFeed reporter and a Financial Times reporter, and company employees, the email from ByteDance general counsel Erich Andersen said. The employees looked at IP addresses of journalists attempting to learn if they were in the same location as employees suspected of leaking confidential information. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

ByteDance says it has fired four employees who accessed the data of several TikTok users located in the US, including journalists. According to The New York Times, an investigation conducted by an outside law firm found that the employees were trying to locate the sources of leaks to reporters. Two of the employees were in the US and two were in China, where ByteDance is based. The company reportedly determined that members of a team responsible for monitoring employee conduct accessed the IP addresses and other data linked to the TikTok accounts of a reporter from BuzzFeed News and Cristina Criddle of the Financial Times. The employees are also said to have accessed the data of several people with ties to the journalists.

Texti is a Natural Language Processing (NLP) AI extension. It allows developers to create sophisticated AI-driven applications that can understand and respond to natural language input from users. Texti's NLP engine can be used for a variety of tasks such as sentiment analysis, question answering, image captioning, and more.

Opinion mining is the branch of computation that deals with opinions, appraisals, attitudes, and emotions of people and their different aspects. This field has attracted substantial research interest in recent years. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. his paper has presented a framework of aspect-based opinion mining based on the concept of transfer learning. on real-world customer reviews available on the Amazon website. The model has yielded quite satisfactory results in its task of aspect-based opinion mining.

Humans are sophisticated at reading interlocutors' emotions from multimodal signals, such as speech contents, voice tones and facial expressions. However, machines might struggle to understand various emotions due to the difficulty of effectively decoding emotions from the complex interactions between multimodal signals. In this paper, we propose a multimodal emotion analysis framework, InterMulti, to capture complex multimodal interactions from different views and identify emotions from multimodal signals. Our proposed framework decomposes signals of different modalities into three kinds of multimodal interaction representations, including a modality-full interaction representation, a modality-shared interaction representation, and three modality-specific interaction representations. Additionally, to balance the contribution of different modalities and learn a more informative latent interaction representation, we developed a novel Text-dominated Hierarchical High-order Fusion(THHF) module. THHF module reasonably integrates the above three kinds of representations into a comprehensive multimodal interaction representation. Extensive experimental results on widely used datasets, (i.e.) MOSEI, MOSI and IEMOCAP, demonstrate that our method outperforms the state-of-the-art.

Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.