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    • Revista IJIMAI
    • 2019
    • vol. 5, nº 5, june 2019
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2019
    • vol. 5, nº 5, june 2019
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    Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

    Autor: 
    Harish, B S
    ;
    Kumar, Keerthi
    ;
    Darshan, H K
    Fecha: 
    06/2019
    Palabra clave: 
    classification; sentiment analysis; hybrid features; short text; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12526
    DOI: 
    http://doi.org/10.9781/ijimai.2018.12.005
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2703
    Open Access
    Resumen:
    Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naïve Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification.
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