Paragraph2Vec-based sentiment analysis on social media for business in Thailand
Parinya Sanguansat · 2016
This paper proposes the sentiment analysis system in Thai language. It aims to use for the three business types (Retail, Banking and Telecommunication) to monitor their brand image via social media. Pantip.com is the most popular online community in Thailand, which many customers posted the comments about their business. Normally, three sentiments must be identified (positive, negative and neutral), but four sentiments (positive, negative, neutral and need) are introduced in our proposed system because the need sentiment can be used for generating new business opportunities. The unsupervised deep learning feature extraction for text, called Paragraph2Vec, paragraph vector or Doc2Vec, was applied in this paper, compared to the classical TF-IDF. The experimental results show that our proposed method perform better than the baseline method.