Sentiment Classification Based on AS-LDA Model

Procedia Computer SciencePublished 1 January 2014Open access

Jiguang Liang, Ping Liu, Jianlong Tan, Shuo Bai

DOI10.1016/j.procs.2014.05.296

Source URL

Citations36

SJR score0.47

SNIP0.90

View PDF

Abstract

We address the task of sentiment classification - identification of the polarity of the subjective document in this paper. We introduces a sentiment classification method called AS LDA. In this model, we assume that words in subjective documents consists of two parts: sentiment element words and auxiliary words which are sampled accordingly from sentiment topics and auxiliary topics. Sentiment element words include targets of the opinions, polarity words and modifiers of polarity words. Experimental results demonstrate that our approach outperforms Latent Dirichlet Allocation (LDA).

Keywords

Computer Science

Thumbs up?

6,987 Citations2002Bo Pang, Lillian Lee +1 more

This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.

View PDF

Learning Word Vectors for Sentiment Analysis

3,299 Citations2011Andrew L. Maas, Raymond E. Daly +4 more

This work presents a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content, and finds it out-performs several previously introduced methods for sentiment classification.

Domain adaptation for large-scale sentiment classification: A deep learning approach

1,563 Citations2011Xavier Glorot, Antoine Bordes +1 more

A deep learning approach is proposed which learns to extract a meaningful representation for each review in an unsupervised fashion and clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products.

Journal of Management Information SystemsEmotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior

1,561 Citations2013Stefan Stieglitz, Linh Dang-Xuan

It is found that emotionally charged Twitter messages tend to be retweeted more often and more quickly compared to neutral ones, and companies should pay more attention to the analysis of sentiment related to their brands and products in social media communication as well as in designing advertising content that triggers emotions.

Joint sentiment/topic model for sentiment analysis

972 Citations2009Chenghua Lin, Yulan He

A novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text is proposed, which is fully unsupervised.

Target-dependent Twitter Sentiment Classification

846 Citations2011Long Jiang, Mo Yu +3 more

This paper proposes to improve target-dependent Twitter sentiment classification by incorporating target- dependent features; and taking related tweets into consideration; and according to the experimental results, this approach greatly improves the performance of target- dependence sentiment classification.

Cross-domain sentiment classification via spectral feature alignment

789 Citations2010Sinno Jialin Pan, Xiaochuan Ni +3 more

This work develops a general solution to sentiment classification when the authors do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain and proposes a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge.

Aspect and sentiment unification model for online review analysis

772 Citations2011Yohan Jo, Alice Oh

This paper proposes Sentence-LDA and extends it to Aspect and Sentiment Unification Model (ASUM), which incorporates aspect and sentiment together to model sentiments toward different aspects and shows that ASUM outperforms other generative models and comes close to supervised classification methods.

Co-training for cross-lingual sentiment classification

499 Citations2009Xiaojun Wan

A cotraining approach is proposed to making use of unlabeled Chinese data for cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data.

View PDF

Characterizing debate performance via aggregated twitter sentiment

473 Citations2010Nicholas Diakopoulos, David A. Shamma

An analytical methodology and visual representations are developed that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video.