Open Access10.30019/IJCLCLP.200706.0001
Using a Generative Model for Sentiment Analysis
Yi Hu,Ruzhan Lu,Yuquan Chen,Jianyong Duan +3 more
- 01 Jun 2007
- Vol. 12, Iss: 2, pp 107-125
TL;DR: A generative model based on the language modeling approach for sentiment analysis that captures the subtle information needed in text retrieval by characterizing the semantic orientation of documents as "favorable" or "unfavorable", which performs better on a Chinese digital product review corpus by a 3-fold cross- validate.
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Abstract: This paper presents a generative model based on the language modeling approach for sentiment analysis. By characterizing the semantic orientation of documents as "favorable" (positive) or "unfavorable" (negative), this method captures the subtle information needed in text retrieval. In order to conduct this research, a language model based method is proposed to keep the dependent link between a "term" and other ordinary words in the context of a triggered language model: first, a batch of terms in a domain are identified; second, two different language models representing classifying knowledge for every term are built up from subjective sentences; last, a classifying function based on the generation of a test document is defined for the sentiment analysis. When compared with Support Vector Machine, a popular discriminative model, the language modeling approach performs better on a Chinese digital product review corpus by a 3-fold cross-validation. This result motivates one to consider finding more suitable language models for sentiment detection in future research.
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Citations
Sentiment analysis and opinion mining: on optimal parameters and performances
TL;DR: In this paper, a novel approach is introduced based on LSTM recurrent neural network language models that do not require any special preprocessing or feature selection, and benchmark results are presented on seven well-known datasets from different domains.
43
Efficient utilization of pre-trained models: A review of sentiment analysis via prompt learning
Kun Bu,Yuanchao Liu,Xiaolong Ju +2 more
TL;DR: This paper reviews the application of prompt learning to sentiment analysis, leveraging pre-trained models to improve performance, and discusses its advantages, including adaptability and innovation, and future research directions.
21
Sentiment detection in micro-blogs using unsupervised chunk extraction
Pierre Magistry,Shu-Kai Hsieh,Yu-Yun Chang +2 more
- 26 Jan 2016
TL;DR: This paper uses an unsupervised Chinese word segmentation system and binomial test to extract specific and endogenous lexicon chunks from the training corpus and combines them with other external resources to train a maximum entropy model for document classification.
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis.
Amr El-Desoky Mousa,Björn Schuller +1 more
- 01 Apr 2017
TL;DR: This work investigates a novel generative approach in which a separate probability distribution is estimated for every sentiment using language models (LMs) based on long short-term memory (LSTM) RNNs, and introduces a novel type of LM using a modified version of bidirectional LSTM (BLSTM).
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