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.

In order to comprehend how the text is organised, syntactic and semantic methods were being used (to identify meaning). Lemmatization, tokenization, and part-of-speech tagging are some of these approaches.After the text has been cleaned up using NLP methods, machine learning algorithms may classify it. Computers could now detect patterns in data and forecast events thanks to machine learning. So instead explicit instructions, machine learning algorithms get their cues from example that are close to them (training data). If you desire your model to be able to classify text according to sentiment, you must train it with examples of textual emotions.

Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability.

Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses). Together with named-entity recognition (NER) and disambiguation (NED), RE forms the basis for many advanced IE tasks such as knowledge-base (KB) population and verification. In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE by encoding structured information about the sentences' principal units, such as subjects, objects, verbal phrases, and adverbials, into various forms of vectorized (and hence unstructured) representations of the sentences. Our main conjecture is that the decomposition of long and possibly convoluted sentences into multiple smaller clauses via OpenIE even helps to fine-tune context-sensitive language models such as BERT (and its plethora of variants) for RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models compared to existing RE approaches. Our best results reach 92% and 71% of F1 score for KnowledgeNet and FewRel, respectively, proving the effectiveness of our approach on competitive benchmarks.