Opinion mining from machine translated Bangla reviews with stacked contractive auto-encoders

• Published in: Journal of ambient intelligence and humanized computing Vol. 14; no. 9; pp. 12119 - 12131
• Main Author: Bodini, Matteo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Classifiers; Coders; Computational Intelligence; Data mining; Datasets; Engineering; English language; Food; Learning; Machine learning; Machine translation; Original Research; Restaurants; Robotics and Automation; Sentiment analysis; User Interfaces and Human Computer Interaction; Auto-encoders; Aspect-based sentiment analysis; Opinion mining; Bangla language; Machine translation; Stacked learning; Text classification
• ISSN: 1868-5137; 1868-5145; 1868-5145
• DOI: 10.1007/s12652-022-03760-w

In the last years, online users have been sharing more and more opinions, reviews, and comments on the web. Opinion mining is the automatic process of getting the subject of such opinions, and recently it has been attracting great commercial and academic interest. Several methods were presented for performing opinion mining in Bangla language, however they reported limited performance. In the present article, we considered the only two publicly datasets available for opinion mining in the Bangla language. We machine translated the datasets into the English language and we preprocessed them by extracting textual frequency based features. Then, we designed two stacked contractive auto-encoders based architectures to perform opinion mining in Bangla language, one for each dataset. The classifiers were trained on the machine translated version on the two datasets in a stacked learning fashion. The proposed classifiers achieved improved performance, with respect to accuracy ( ≥ 96 % ), precision ( ≥ 93 % ), recall ( ≥ 94 % ), and F1 score ( ≥ 94 % ), reported in the past state of the art works. Furthermore, the experimental results showed that both the machine translation procedure and the stacked learning frameworks improved the final classification performance.