Abstract
Due to increase of online product reviews posted daily through various modalities such as video, audio and text, sentimental analysis has gained huge attention. Recent developments in web technologies have also enabled the increase of web content in Hindi. In this paper, an approach to detect the sentiment of an online Hindi product reviews based on its multi-modality natures (audio and text) is presented. For each audio input, Mel Frequency Cepstral Coefficients (MFCC) features are extracted. These features are used to develop a sentiment models using Gaussian Mixture Models (GMM) and Deep Neural Network (DNN) classifiers. From results, it is observed that DNN classifier gives better results compare to GMM. Further textual features are extracted from the transcript of the audio input by using Doc2vec vectors. Support Vector Machine (SVM) classifier is used to develop a sentiment model using these textual features. From experimental results it is observed that combining both the audio and text features results in improvement in the performance for detecting the sentiment of an online product reviews.
R. Prasath—A part of this was carried out when the author was in Indian Institute of Information Technology (IIIT) Sricity, India.
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References
Chaovalit, P., Zhou, L.: Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of IEEE 38th Hawaii International Conference on System Sciences, Big Island, Hawaii, pp. 1–9 (2005)
Gamallo, P., Garcia, M.: Citius: a naive-bayes strategy for sentiment analysis on english tweets. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 171–175, August 2014
Kaushik, L., Sangwan, A., Hansen, J.H.L.: Sentiment extraction from natural audio streams. In: Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 8485–8489 (2013)
Kaushik, L., Sangwan, A., Hansen, J.H.: Automatic audio sentiment extraction using keyword spotting. In: Proceedings of Interspeech, pp. 2709–2713, September 2015
Kumar, A., Sebastian, T.M.: Sentiment analysis on twitter. IJCSI Int. J. Comput. Sci. 9(4), 372–378 (2012)
Mairesse, F., Polifroni, J., Fabbrizio, G.D.: Can prosody inform sentiment analysis? experiments on short spoken reviews. In: Proceedings of IEEE International Confernce on Acoustics, Speech and Signal processing (ICASSP), pp. 5093–5096 (2012)
Wollmer, M., Felix, W., Knaup, T., Morency, L.P.: YouTube movie reviews: sentiment analysis in an audio-visual context. IEEE Intll. Syst. 28(3), 46–53 (2013)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014)
Morency, L.P., Mihalcea, R., Doshi, P.: Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th International Conference on Multimodal Interfaces (ICMI2011), pp. 169–176, November 2011
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)
Perez-Rosas, V., Mihalcea, R., Morency, L.P.: Multimodal sentiment analysis of spanish online videos. IEEE Intll. Syst. 28(3), 38–45 (2013)
Perez-Rosas, V., Mihalcea, R., Morency, L.P.: Utterance level multimodal sentiment analysis. In: Proceedings of ACL, pp. 973–982 (2013)
Poria, S., Cambria, E., Gelbukh, A.: Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: Proceedings of EMNLP, pp. 2539–2544 (2015)
Poria, S., Cambria, E., Howard, N., Huang, G.B., Hussain, A.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2015)
Xing, L., Yuan, L., Qinglin, W., Yu, L.: An approach to sentiment analysis of short chinese texts based on SVMs. In: Proceedings of the 34th Chinese Control Conference, pp. 28–30. IEEE, July 2015
Yadav, S.K., Bhushan, M., Gupta, S.: Multimodal sentiment analysis: sentiment analysis using audiovisual format. In: Proceedings of IEEE 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1415–1419 (2015)
Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of IEEE International Conference on Data Mining (ICDM) (2003)
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Abburi, H., Prasath, R., Shrivastava, M., Gangashetty, S.V. (2017). Multimodal Sentiment Analysis Using Deep Neural Networks. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_6
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DOI: https://doi.org/10.1007/978-3-319-58130-9_6
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