• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.142-151
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• 2023

Sentiment analysis is a method used to comprehend feelings, opinions, and attitudes in text, and it is essential for evaluating consumer feedback and social media posts. However, creating sentiment dictionaries, which are necessary for this analysis, is complex and time-consuming because people express their emotions differently depending on the context and domain. In this study, we propose a new method for simplifying this procedure. We utilize syntax analysis of the Korean language to identify and extract sentiment words based on the Reason-Sentiment Pattern, which distinguishes between words expressing feelings and words explaining why those feelings are expressed, making it applicable in various contexts and domains. We also define sentiment words as those with clear polarity, even when used independently and exclude words whose polarity varies with context and domain. This approach enables the extraction of explicit sentiment expressions, enhancing the accuracy of sentiment analysis at the attribute level. Our methodology, validated using Korean cosmetics review datasets from Korean online shopping malls, demonstrates how a sentiment dictionary focused solely on clear polarity words can provide valuable insights for product planners. Understanding the polarity and reasons behind specific attributes enables improvement of product weaknesses and emphasis on strengths. This approach not only reduces dependency on extensive sentiment dictionaries but also offers high accuracy and applicability across various domains.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.3
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• pp.291-299
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• 2013

Most sentiment analysis systems count the number of occurrences of sentiment expressions in a text, and evaluate the text by summing polarity values of extracted sentiment expressions. However, linguistic contexts of the expressions should be taken into account in order to analyze sentimental orientation of the text meticulously. Korean auxiliary predicates affect meaning of the main verb or adjective in some ways while attached to it in their usage. In this paper, we introduce a new approach that handles Korean auxiliary predicates in the light of sentiment analysis. We classify the auxiliary predicates according to their strength of impact on sentiment polarity values. We also define compositional rules of auxiliary predicates to update polarity values when the predicates appear along with sentiment expressions. This approach is implemented to a sentiment analysis system to extract opinions about a specific individual from review documents which were collected from various web sites. An experimental result shows approximately 72.6% precision and 52.7% recall for correctly detecting sentiment expressions from a text.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• ETRI Journal
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• v.35 no.5
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• pp.838-848
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• 2013

This paper presents a novel approach to automatically generate Korean multiword sentiment expressions by using a seed sentiment lexicon and a large-scale domain-specific corpus. A multiword sentiment expression consists of a seed sentiment word and its contextual words occurring adjacent to the seed word. The multiword sentiment expressions that are the focus of our study have a different polarity from that of the seed sentiment word. The automatically extracted multiword sentiment expressions show that 1) the contextual words should be defined as a part of a multiword sentiment expression in addition to their corresponding seed sentiment word, 2) the identified multiword sentiment expressions contain various indicators for polarity shift that have rarely been recognized before, and 3) the newly recognized shifters contribute to assigning a more accurate polarity value. The empirical result shows that the proposed approach achieves improved performance of the sentiment analysis system that uses an automatically generated lexicon.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.53-60
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• 2020

Sentiment analysis is the automated process of understanding attitudes and opinions about a given topic from written or spoken text. One of the sentiment analysis approaches is a dictionary-based approach, in which a sentiment dictionary plays an much important role. In this paper, we propose a method to automatically generate Korean sentiment lexicon from the well-known English sentiment lexicon called VADER (Valence Aware Dictionary and sEntiment Reasoner). The proposed method consists of three steps. The first step is to build a Korean-English bilingual lexicon using a Korean-English parallel corpus. The bilingual lexicon is a set of pairs between VADER sentiment words and Korean morphemes as candidates of Korean sentiment words. The second step is to construct a bilingual words graph using the bilingual lexicon. The third step is to run the label propagation algorithm throughout the bilingual graph. Finally a new Korean sentiment lexicon is generated by repeatedly applying the propagation algorithm until the values of all vertices converge. Empirically, the dictionary-based sentiment classifier using the Korean sentiment lexicon outperforms machine learning-based approaches on the KMU sentiment corpus and the Naver sentiment corpus. In the future, we will apply the proposed approach to generate multilingual sentiment lexica.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2759-2769
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• 2018

The behavioral finance view on the existence of asset pricing anomalies is based on two factors: investors' sentiment and limits to arbitrage. This paper tries to examine the effect of investors' sentiment on the stock price in the Korean stock market. In order to measure investors' sentiment, we constructed the sentiment index using principal component of five sentiment variables. By using sentiment index as an additional independent variable to three risk factors, impacts of the sentiment index on individual stocks and 25 portfolios sorted by BM-size are examined. Main results found are as follows: 1) not only all three risk factors show positive impacts on the return of individual stock, but also the sentiment index has a positive impact. SI alone explains 15% of individual return variation. 2) among four independent variables, the most important factor turned out to be the market risk factor and investors' sentiment has better explanatory power on stock price than the size effect. 3) after controlling the market risk factor, the coefficient of the sentiment index for the smallest size and highest book/market value portfolios is significantly positive. 4) all the coefficients of the sentiment index for 25 portfolios sorted by BM-size have significant positive value after controlling size or (and) value.

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.71-78
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• 2014

We propose the sentiment pattern as a novel sentiment feature for more accurate text sentiment analysis, and introduce the rating inference of movie reviews using it. The text sentiment analysis is a task that recognizes and classifies sentiment of text whether it is positive or negative. For that purpose, the sentiment feature is used, which includes sentiment words and phrase pattern that have specific sentiment like positive or negative. The previous researches for the sentiment analysis, however, have a limit to understand accurately total sentiment of either a sentence or text because they consider the sentiment of sentiment words and phrase patterns independently. Therefore, we propose the sentiment pattern that is defined by arranging semantically all sentiment in a sentence, and use them as a new sentiment feature for the rating inference that is one of the detail subjects of the sentiment analysis. In order to verify the effect of proposed sentiment pattern, we conducted experiments of rating inference. Ratings of test reviews is inferred by using a probabilistic method with sentiment features including sentiment patterns extracted from training reviews. As a result, it is shown that the result of rating inference with sentiment patterns are more accurate than that without sentiment patterns.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.79-89
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• 2018

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phoneme-level sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.