Abstract
Sentiment analysis algorithms are often used for sentiment polarity classification and topic classification. Sentiment polarity classification is used a lot for review analysis. Gross prediction of the movie combined with people’s anticipations, if available before release of the movie, can hugely benefit the producer as well as the distributor in increasing their profits. In this paper, we particularly focus on gross prediction for the movie and the reliability of that prediction by analyzing user reviews on various prerelease events. For that, we propose a modified approach built on top of word2vec achieving comparable results to doc2vec, with significantly less running time and space complexity than doc2vec.
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References
Maas AL et al (2011) Learning word vectors for sentiment analysis. ACL
Rajaraman A, Ullman J (2011) Mining of massive datasets. Cambridge University Press, pp 1–17
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. ICLR Workshop
Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on machine learning, PMLR 32(2):1188–1196
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning technique. In: Proceedings of EMNLP, pp 79–86
Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques (The Morgan Kaufmann Series in Data Management Systems), 2nd edn. Morgan Kaufmann, San Francisco, CA, USA
Ng A (2016) Machine learning yearning: draft version 0.5
Malouf R (2002) A Comparison of algorithms for maximum entropy parameter estimation. In: Sixth conference on natural language learning (CoNLL), pp 49–55
Potts C (2011) On the negativity of negation
Sharma V, Rai S, Dev A (2012) Compr Study Artif Neural Netw 2(10)
Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd ACL, pp 271–278
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Parikh, Y., Palusa, A., Kasthuri, S., Mehta, R., Rana, D. (2018). Efficient Word2Vec Vectors for Sentiment Analysis to Improve Commercial Movie Success. In: Bhattacharyya, S., Gandhi, T., Sharma, K., Dutta, P. (eds) Advanced Computational and Communication Paradigms. Lecture Notes in Electrical Engineering, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-10-8240-5_30
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DOI: https://doi.org/10.1007/978-981-10-8240-5_30
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