Book ChapterDOI
Grammar Rule-Based Sentiment Categorization Model for Tamil Tweets
Nadana Ravishankar,R. Shriram,K. Vengatesan,S. B. Mahajan,Padmanaban Sanjeevikumar,Subramaniam Umashankar +5 more
- pp 687-695
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TLDR
This work intends to find the polarity of Tamil tweets in addition to genre classification by developing a model to mine user tweets collected from Twitter using modified N-gram approach to predict the sentiments of the users in the dataset.Abstract:
The widespread of social media is growing every day where users are sharing their opinions, reviews, and comments on an item or product. The aim is to develop a model to mine user tweets collected from Twitter. In this paper, our contribution on user tweets to find the sentiments expressed by users about Tamil movies based on the grammar rule. Tamil movies domain is selected to confine our scope of the work. After preprocessing, N-gram approach is applied to classify tweets into different genres. This work intends to find the polarity of Tamil tweets in addition to genre classification. In this work, it is also shown how to collect user tweets which comes as data stream using modified N-gram approach to predict the sentiments of the users in the dataset. Results suggest that N-gram model not only remove the complexity of natural language process but also help to improve the decision-making process.read more
Citations
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Review of Parallelization Analysis of the PEEC-Based Solver
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Proceedings ArticleDOI
Extraction of Sentiments in Tamil Sentences Using Deep Learning
TL;DR: In this article , an approach using Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Simple Deep Neural Network (SDFN) techniques was proposed for sentiment analysis of Tamil text.
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ELSA: Ensemble learning based sentiment analysis for diversified text
C. Kumaresan,P. Thangaraju +1 more
TL;DR: In this article , the authors presented an ensemble technique by collaborating Generative Adversarial Network (GAN) and Self-Attention Network (SAN) to analyze the tweets that contains diversified languages.
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Proceedings ArticleDOI
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