A Pre-LN Transformer Network Model with Lexical Features for Fine-Grained Sentiment Classification

Objective To create a sentiment classification system for the Fifth i2b2/VA Challenge Track 2, which can identify thirteen subjective categories and two objective categories. Design We developed a hybrid system using Support Vector Machine (SVM) classifiers with augmented training data from the Internet. Our system consists of three types of classification-based systems: the first system uses spanning n-gram features for subjective categories, the second one uses bag-of-n-gram features for objective categories, and the third one uses pattern matching for infrequent or subtle emotion categories. The spanning n-gram features are selected by a feature selection algorithm that leverages emotional corpus from weblogs. Special normalization of objective sentences is generalized with shallow parsing and external web knowledge. We utilize three sources of web data: the weblog of LiveJournal which helps to improve the feature selection, the eBay List which assists in special normalization of information and instructions categories, and the suicide project web which provides unlabeled data with similar properties as suicide notes. Measurements The performance is evaluated by the overall micro-averaged precision, recall and F-measure. Result Our system achieved an overall micro-averaged F-measure of 0.59. Happiness_peacefulness had the highest F-measure of 0.81. We were ranked as the second best out of 26 competing teams. Conclusion Our results indicated that classifying fine-grained sentiments at sentence level is a non-trivial task. It is effective to divide categories into different groups according to their semantic properties. In addition, our system performance benefits from external knowledge extracted from publically available web data of other purposes; performance can be further enhanced when more training data is available.

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Research on Fine-Grained Sentiment Classification

Natural Language Processing and Chinese Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-32236-6_39 ◽

2019 ◽

pp. 435-444 ◽

Cited By ~ 1

Author(s):

Zhihui Wang ◽

Xiaodong Wang ◽

Tao Chang ◽

Shaohe Lv ◽

Xiaoting Guo

Keyword(s):

Sentiment Classification ◽

Fine Grained

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Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges

Applied Computational Intelligence and Soft Computing ◽

10.1155/2014/735942 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 18

Author(s):

Mita K. Dalal ◽

Mukesh A. Zaveri

Keyword(s):

Opinion Mining ◽

Binary Classification ◽

Empirical Studies ◽

Online Reviews ◽

Sentiment Classification ◽

Mining System ◽

User Reviews ◽

Fine Grained ◽

Feature Based ◽

Linguistic Hedges

Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.

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Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6517 ◽

2020 ◽

Vol 34 (05) ◽

pp. 9685-9692

Author(s):

Yaowei Zheng ◽

Richong Zhang ◽

Samuel Mensah ◽

Yongyi Mao

Keyword(s):

Neural Network ◽

Random Walk ◽

Network Model ◽

Neural Network Model ◽

Syntactic Structure ◽

Contextual Information ◽

Empirical Studies ◽

Specific Aspect ◽

Sentiment Classification ◽

Benchmark Datasets

Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the representation for the classification.

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A Survey on Aspect-Based Sentiment Classification

ACM Computing Surveys ◽

10.1145/3503044 ◽

2021 ◽

Author(s):

Gianni Brauwers ◽

Flavius Frasincar

Keyword(s):

State Of The Art ◽

Knowledge Bases ◽

Sentiment Classification ◽

Automatic Extraction ◽

Learning Models ◽

Text Documents ◽

Fine Grained ◽

Knowledge Based ◽

Transformer Model ◽

The Web

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

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