Abstract
Opinions and sentiments are essential to human activities and have a wide variety of applications. As many decision makers turn to social media due to large volume of opinion data available, efficient and accurate sentiment analysis is necessary to extract those data. Hence, text sentiment analysis has recently become a popular field and has attracted many researchers. However, extracting sentiments from audio speech remains a challenge. This project explored the possibility of applying supervised Machine Learning in recognizing sentiments in English utterances on a sentence level. In addition, the project also aimed to examine the effect of combining acoustic and linguistic features on classification accuracy. Six audio tracks were randomly selected to be training data from 40 YouTube videos (monologue) with strong presence of sentiments. Speakers expressed sentiments towards products, films, or political events. These sentiments were manually labelled as negative and positive based on independent judgment of three experimenters. A wide range of acoustic and linguistic features were then analyzed and extracted using sound editing and text mining tools, respectively. A novel approach was proposed, which used a simplified sentiment score to integrate linguistic features and estimate sentiment valence. This approach improved negation analysis and hence increased overall accuracy. Results showed that when both linguistic and acoustic features were used, accuracy of sentiment recognition improved significantly, and that excellent prediction was achieved when the four classifiers were trained, respectively, namely, kNN, SVM, Neural Network, and Naïve Bayes. Possible sources of error and inherent challenges of audio sentiment analysis were discussed to provide potential directions for future research.
L. Zhang—Yale-NUS College, Singapore
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Notes
- 1.
A word or lexical unit that has several or multiple meanings.
- 2.
Multilinguality is a characteristic of tasks that involve the use of more than one natural language (Kay, n.d.).
- 3.
Exophoric reference is referring to a situation or entities outside the text. (University of Pennsylvania, 2006).
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Acknowledgment
I would like to express my sincere gratitude to my project supervisor Professor Eddie Ng for being open-minded about my project topic, without which I could not have been able to delve deep into my field of interest. His insightful suggestions and unwavering support has guided me through doubts and difficulties.
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Zhang, L., Ng, E.Y.K. (2019). When Siri Knows How You Feel: Study of Machine Learning in Automatic Sentiment Recognition from Human Speech. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_41
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