Book ChapterDOI
Comparative Evaluation of Various Feature Weighting Methods on Movie Reviews
S. Sivakumar,R. Rajalakshmi +1 more
- pp 721-730
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TLDR
This work concludes that Word2Vec with SGD is the best combination for sentiment classification problem on IMDB dataset and can be used as a base for future exploration of opinioned value on any textual data.Abstract:
Sentiment analysis is a method of extracting subjective information from customer reviews. The analysis helps to reveal the consumer insights about the product, a theme, or a service. In the existing literature, various methods such as BoW and TF-IDF are employed for sentiment analysis and deep learning methods are not explored much. We made an attempt to apply Word2Vec feature weighting method for this problem. We carried out various experiments for sentiment analysis on a large dataset IMDB that contains movie review. We compared various feature weighting methods and analyzed using different classifiers, and the best combination was determined. From the experimental results, we conclude that Word2Vec with SGD is the best combination for sentiment classification problem on IMDB dataset. The result shown in the paper can be used as a base for future exploration of opinioned value on any textual data.read more
Citations
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Review on Word2Vec Word Embedding Neural Net
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Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews
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Transfer Learning Approach for Identification of Malicious Domain Names.
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Incorporating feature representation into BiLSTM for deceptive review detection
Wentao Liu,Weipeng Jing,Yang Li +2 more
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References
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Proceedings Article
Learning Word Vectors for Sentiment Analysis
TL;DR: This work presents a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content, and finds it out-performs several previously introduced methods for sentiment classification.
Proceedings Article
Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
TL;DR: It is shown that the inclusion of word bigram features gives consistent gains on sentiment analysis tasks, and a simple but novel SVM variant using NB log-count ratios as feature values consistently performs well across tasks and datasets.
Proceedings ArticleDOI
Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification
TL;DR: An aspect oriented scheme that analyses the textual reviews of a movie and assign it a sentiment label on each aspect and produces a more accurate and focused sentiment profile than the simple document-level sentiment analysis.
Proceedings ArticleDOI
Movie review analysis: emotion analysis of IMDb movie reviews
Kamil Topal,Gultekin Ozsoyoglu +1 more
TL;DR: This paper argues that there is a better way: reviewers movie scores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie.
Posted Content
Feature Weight Tuning for Recursive Neural Networks.
TL;DR: This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition.