1439 papers with code • 40 benchmarks • 99 datasets Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can...
Sentiment analysis comes under the umbrella of Natural Language Processing, click here to read about the best and free resources to get started with NLP. Sentiment analysis is like a gateway to AI based text analysis. For any company or data scientist looking to extract meaning out of an ...
Sentiment analysis is a fundamental and valuable task in NLP. However, due to limitations in data and technological availability, research into sentiment analysis of African languages has been fragmented and lacking. With the recent release of the AfriSenti-SemEval Shared Task 12, hosted as a part...
Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8. Mozhi Zhang, Hang Yan, Yaqian Zhou, and Xipeng Qiu. 2023a. Promptner: A prompting method for fewshot named entity recognition via k nearest neighbor search. ar...
Our research integrates an extensive range of linguistic features, such as syntactic dependencies and part-of-speech patterns, into the ABSA framework. This integration substantially enhances the model’s ability to capture the nuances of language, leading to improved sentiment analysis accuracy. ...
Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opinion words, such as ''good'' and ''bad''. However, implicit sentiment widely exists in the ABSA dataset, which refers ...
Sentiment analysis analyzes the subjective information in an expression. For example, opinions, appraisals, emotions, or attitudes toward a topic, person, or entity. Expressions are classified, with a confidence score, as positive, negative, or neutral. ...
Pang et al. [13] were the first to research sentiment analysis using a machine-learning technique. They conducted tests on a movie review dataset using supervised classifiers such as Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM). The classifiers perform poorly in classifying senti...
, Sentiment Analysis in Social Networks, Morgan Kaufmann) (2017), pp. 71-90, 10.1016/B978-0-12-804412-4.00005-X Google Scholar 134 M.V. Mäntylä, D. Graziotin, M. Kuutila The evolution of sentiment analysis—A review of research topics, venues, and top cited papers Computer ...
Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6,996 papers from ...