Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey

Zhang, Hailong; Gan, Wenyan; Jiang, Bo

doi:10.1109/wisa.2014.55

Cited by 96 publications

(45 citation statements)

References 19 publications

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“…These approaches adopt a lexicon to perform sentiment analysis by counting and weighting sentiment words that have been evaluated and tagged [12]. Nasukawa and Yi [13] developed a method to determine subject favorability by creating a sentiment lexicon containing 3513 sentiment terms.…”

Section: Sentiment Classificationmentioning

confidence: 99%

“…Both these studies used the relationship between words in a knowledge base. The main strategy in these methods is to first manually collect an initial seed set of sentiment words and their orientations and then search for their synonyms and antonyms in a knowledge base to expand this set [12]. However, very few complete and robust knowledge bases are available for the Chinese language.…”

Section: Lexicon Creationmentioning

confidence: 99%

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Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

Feng

Gong

et al. 2018

Wireless Communications and Mobile Computing

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The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users' product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of highquality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain-specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non-domain-specific sentiment lexicons as well as state-of-the-art machine-learning and deep-learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.

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Section: Sentiment Classificationmentioning

confidence: 99%

Section: Lexicon Creationmentioning

confidence: 99%

Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

Feng

Gong

et al. 2018

Wireless Communications and Mobile Computing

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“…To bridge the gap, there have been attempts to translate some open source English resources into other languages by using the machine translation service. But every language has its peculiarities, which may render the adapting technique unsuitable, so one of the challenges of SA is to create specific linguistic resources for different languages (Balahur et al 2014;Montoyo et al 2012;Ravi & Ravi 2015;Taboada 2016;Zhang et al 2014).…”

Section: Identifying Polarity In Different Text Typesmentioning

confidence: 99%

“…Both the lexicon-based and the machine-learning approach have yielded good results: for English texts, the accuracy is mostly between 70-90%, while higher scores have been achieved with texts sharing a domain (e.g., movie reviews), when subjected to a two-way classification (into positive and negative) (Pang & Lee 2008;Taboada et al 2011;Zhang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Identifying Polarity in Different Text Types

Pajupuu¹,

Altrov²,

Pajupuu³

2016

FEJF

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While Sentiment Analysis aims to identify the writer's attitude toward individuals, events or topics, our aim is to predict the possible effect of a written text on the reader. For this purpose, we created an automatic identifier of the polarity of Estonian texts, which is independent of domain and of text type. Depending on the approach chosen -lexicon-based or machine learning -the identifier uses either a lexicon of words with a positive or negative connotation, or a text corpus where orthographic paragraphs have been annotated as positive, negative, neutral or mixed. Both approaches worked well, resulting in a nearly 75% accuracy on average. It was found that in some cases the results depend on the text type, notably, with sports texts the lexicon-based approach yielded a maximum accuracy of 80.3%, while over 88% was gained for opinion stories approached by machine learning.

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