• Title, Summary, Keyword: Sentiment Classification

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A Korean Sentence and Document Sentiment Classification System Using Sentiment Features (감정 자질을 이용한 한국어 문장 및 문서 감정 분류 시스템)

  • Hwang, Jaw-Won;Ko, Young-Joong
    • Journal of KIISE:Computing Practices and Letters
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    • v.14 no.3
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    • pp.336-340
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    • 2008
  • Sentiment classification is a recent subdiscipline of text classification, which is concerned not with the topic but with opinion. In this paper, we present a Korean sentence and document classification system using effective sentiment features. Korean sentiment classification starts from constructing effective sentiment feature sets for positive and negative. The synonym information of a English word thesaurus is used to extract effective sentiment features and then the extracted English sentiment features are translated in Korean features by English-Korean dictionary. A sentence or a document is represented by using the extracted sentiment features and is classified and evaluated by SVM(Support Vector Machine).

A Method for User Sentiment Classification using Instagram Hashtags (인스타그램 해시태그를 이용한 사용자 감정 분류 방법)

  • Nam, Minji;Lee, EunJi;Shin, Juhyun
    • Journal of Korea Multimedia Society
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    • v.18 no.11
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    • pp.1391-1399
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    • 2015
  • In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

An Improved Text Classification Method for Sentiment Classification

  • Wang, Guangxing;Shin, Seong Yoon
    • Journal of information and communication convergence engineering
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    • v.17 no.1
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    • pp.41-48
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    • 2019
  • In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

Empirical Sentiment Classification Using Psychological Emotions and Social Web Data (심리학적 감정과 소셜 웹 자료를 이용한 감성의 실증적 분류)

  • Chang, Moon-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.5
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    • pp.563-569
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    • 2012
  • The studies of opinion mining or sentiment analysis have been the focus with social web proliferation. Sentiment analysis requires sentiment resources to decide its polarity. In the existing sentiment analysis, they have been built resources designed with intensity of sentiment polarity and decided polarity of opinion using the ones. In this paper, I will present sentiment categories for not only polarity of opinion but also the basis of positive/negative opinion. I will define psychological emotions to primary sentiments for the reasonable classification. And I will extract the informations of sentiment from social web texts for the actual distribution of sentiments in social web. Re-classifying primary sentiments based on extracted sentiment information, I will organize sentiment categories for the social web. In this paper, I will present 23 categories of sentiment by using proposed method.

The Construction of a Domain-Specific Sentiment Dictionary Using Graph-based Semi-supervised Learning Method (그래프 기반 준지도 학습 방법을 이용한 특정분야 감성사전 구축)

  • Kim, Jung-Ho;Oh, Yean-Ju;Chae, Soo-Hoan
    • Science of Emotion and Sensibility
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    • v.18 no.1
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    • pp.103-110
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    • 2015
  • Sentiment lexicon is an essential element for expressing sentiment on a text or recognizing sentiment from a text. We propose a graph-based semi-supervised learning method to construct a sentiment dictionary as sentiment lexicon set. In particular, we focus on the construction of domain-specific sentiment dictionary. The proposed method makes up a graph according to lexicons and proximity among lexicons, and sentiments of some lexicons which already know their sentiment values are propagated throughout all of the lexicons on the graph. There are two typical types of the sentiment lexicon, sentiment words and sentiment phrase, and we construct a sentiment dictionary by creating each graph of them and infer sentiment of all sentiment lexicons. In order to verify our proposed method, we constructed a sentiment dictionary specific to the movie domain, and conducted sentiment classification experiments with it. As a result, it have been shown that the classification performance using the sentiment dictionary is better than the other using typical general-purpose sentiment dictionary.

Statistical Approach to Sentiment Classification using MapReduce (맵리듀스를 이용한 통계적 접근의 감성 분류)

  • Kang, Mun-Su;Baek, Seung-Hee;Choi, Young-Sik
    • Science of Emotion and Sensibility
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    • v.15 no.4
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    • pp.425-440
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    • 2012
  • As the scale of the internet grows, the amount of subjective data increases. Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary. The former sentiment classification researches have two critical problems. First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data.

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Comparative Study of Various Machine-learning Features for Tweets Sentiment Classification (트윗 감정 분류를 위한 다양한 기계학습 자질에 대한 비교 연구)

  • Hong, Cho-Hee;Kim, Hark-Soo
    • The Journal of the Korea Contents Association
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    • v.12 no.12
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    • pp.471-478
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    • 2012
  • Various studies on sentiment classification of documents have been performed. Recently, they have been applied to twitter sentiment classification. However, they did not show good performances because they did not consider the characteristics of tweets such as tweet structure, emoticons, spelling errors, and newly-coined words. In this paper, we perform experiments on various input features (emoticon polarity, retweet polarity, author polarity, and replacement words) which affect twitter sentiment classification model based on machine-learning techniques. In the experiments with a sentiment classification model based on a support vector machine, we found that the emoticon polarity features and the author polarity features can contribute to improve the performance of a twitter sentiment classification model. Then, we found that the retweet polarity features and the replacement words features do not affect the performance of a twitter sentiment classification model contrary to our expectations.

Attention Capsule Network for Aspect-Level Sentiment Classification

  • Deng, Yu;Lei, Hang;Li, Xiaoyu;Lin, Yiou;Cheng, Wangchi;Yang, Shan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.4
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    • pp.1275-1292
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    • 2021
  • As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

A Korean Document Sentiment Classification System based on Semantic Properties of Sentiment Words (감정 단어의 의미적 특성을 반영한 한국어 문서 감정분류 시스템)

  • Hwang, Jae-Won;Ko, Young-Joong
    • Journal of KIISE:Software and Applications
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    • v.37 no.4
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    • pp.317-322
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    • 2010
  • This paper proposes how to improve performance of the Korean document sentiment-classification system using semantic properties of the sentiment words. A sentiment word means a word with sentiment, and sentiment features are defined by a set of the sentiment words which are important lexical resource for the sentiment classification. Sentiment feature represents different sentiment intensity in general field and in specific domain. In general field, we can estimate the sentiment intensity using a snippet from a search engine, while in specific domain, training data can be used for this estimation. When the sentiment intensity of the sentiment features are estimated, it is called semantic orientation and is used to estimate the sentiment intensity of the sentences in the text documents. After estimating sentiment intensity of the sentences, we apply that to the weights of sentiment features. In this paper, we evaluate our system in three different cases such as general, domain-specific, and general/domain-specific semantic orientation using support vector machine. Our experimental results show the improved performance in all cases, and, especially in general/domain-specific semantic orientation, our proposed method performs 3.1% better than a baseline system indexed by only content words.

A Syllable Kernel based Sentiment Classification for Movie Reviews (음절 커널 기반 영화평 감성 분류)

  • Kim, Sang-Do;Park, Seong-Bae;Park, Se-Young;Lee, Sang-Jo;Kim, Kweon-Yang
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.2
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    • pp.202-207
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    • 2010
  • In this paper, we present an automatic sentiment classification method for on-line movie reviews that do not contain explicit sentiment rating scores. For the sentiment polarity classification, positive or negative, we use a Support Vector Machine classifier based on syllable kernel that is an extended model of string kernel. We give some experimental results which show that proposed syllable kernel model can be effectively used in sentiment classification tasks for on-line movie reviews that usually contain a lot of grammatical errors such as spacing or spelling errors.