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Register a new userSANA : Sentiment Analysis on Newspape
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Added by
Mahieddine Djoudi
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
It is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who is the purpose is finding the behind phenomenon masked in opinionated texts. We are interested in our work by comments in Algerian newspaper websites. For this end, two corpora were used SANA and OCA. SANA corpus is created by collection of comments from three Algerian newspapers, and annotated by two Algerian Arabic native speakers, while OCA is a freely available corpus for sentiment analysis. For the classification we adopt Supports vector machines, naive Bayes and knearest neighbors. Obtained results are very promising and show the different effects of stemming in such domain, also knearest neighbors give important improvement comparing to other classifiers unlike similar works where SVM is the most dominant. From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works.
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With the development of the E-commerce and reviews website, the comment information is influencing peoples life. More and more users share their consumption experience and evaluate the quality of commodity by comment. When people make a decision, they will refer these comments. The dependency of the comments make the fake comment appear. The fake comment is that for profit and other bad motivation, business fabricate untrue consumption experience and they preach or slander some products. The fake comment is easy to mislead users opinion and decision. The accuracy of humans identifying fake comment is low. Its meaningful to detect fake comment using natural language processing technology for people getting true comment information. This paper uses the sentimental analysis to detect fake comment.
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Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.
Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information which may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases.
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