A joint model for topic-sentiment modeling from text

Abstract

Traditional topic models, like LDA and PLSA, have been efficiently extended to capture further aspects of text in addition to the latent topics (e.g., time evolution, sentiment etc.). In this paper, we discuss the issue of joint topicsentiment modeling. We propose a novel topic model for topic-specific sentiment modeling from text and we derive an inference algorithm based on the Gibbs sampling process. We also propose a method for automatically setting the model parameters. The experiments performed on two review datasets show that our model outperforms other stateof-the-art models, in particular for sentiment prediction at the topic level.

Sentiment analysis on social media for stock movement prediction

“…There are some researches trying to extract both topic and sentiment for other domains such as online product review, restaurant review and movie review dataset (Dermouche et al, 2015). Jo and Oh proposed ASUM model for 170 extracting both aspect and sentiment for online product review dataset (Jo & Oh, 2011).…”

Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters

“…2 https://developers.google.com/maps/ TS: Topic-Sentiment model proposed in (Dermouche et al, 2015). LDST-w/oS: This is the LDST model without employing prior knowledge (seed words).…”

“…Some recent work incorporates context information into LDA, such as time (Wang and McCallum, 2006;Zhao et al, 2014) and authorship (Steyvers et al, 2004;Yang et al, 2016) to make topic models fit expectations better. Some studies also attempt to detect sentiment and topic simultaneously from documents (Dermouche et al, 2015;Mukherjee et al, 2014;. Nevertheless, none of existing methods takes advantage of temporal and geographical information to identify and track people's topics and sentiment during emergencies and disasters.…”

Enriched Latent Dirichlet Allocation for Sentiment Analysis

“…Reverse‐JST (RJST; Lin et al, ) is similar to JST with the only difference that sentiment parameter affects the topic in JST, whereas the topic parameter affects the sentiment in RJST, and RJST performed poorly. Topic Sentiment (TS) modelling (Dermouche, Kouas, Velcin, & Loudcher, ) is similar to RJST, with the only difference that there is a distribution of topic on sentiment for each document in RJST, whereas there is only one distribution of topic on sentiment for all documents in TS. The Sequential Reverse Joint Sentiment‐Topic (SRJST; Liang & Wang, ) model proposes a method for the monitoring of user‐generated reviews in terms of both topics and topic‐specific sentiments for online products and services.…”