• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• 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 KIISE:Software and Applications
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• v.37 no.12
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• pp.946-950
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• 2010

Many sentiment categorization systems based on machine learning methods use morphological analyzers in order to extract linguistic features from sentences. However, the morphological analyzers do not generally perform well in a customer review domain because online customer reviews include many spacing errors and spelling errors. These low performances of the underlying systems lead to performance decreases of the sentiment categorization systems. To resolve this problem, we propose a feature extraction method based on simple longest matching of Eojeol (a Korean spacing unit) and phoneme patterns. The two kinds of patterns are automatically constructed from a large amount of POS (part-of-speech) tagged corpus. Eojeol patterns consist of Eojeols including content words such as nouns and verbs. Phoneme patterns consist of leading consonant and vowel pairs of predicate words such as verbs and adjectives because spelling errors seldom occur in leading consonants and vowels. To evaluate the proposed method, we implemented a sentiment categorization system using a SVM (Support Vector Machine) as a machine learner. In the experiment with Korean customer reviews, the sentiment categorization system using the proposed method outperformed that using a morphological analyzer as a feature extractor.

• ETRI Journal
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• v.38 no.3
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• pp.494-501
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• 2016

Sentiment analysis incorporates natural language processing and artificial intelligence and has evolved as an important research area. Sentiment analysis on product reviews has been used in widespread applications to improve customer retention and business processes. In this paper, we propose a method for performing an intensified sentiment analysis on customer product reviews. The method involves the extraction of two feature sets from each of the given customer product reviews, a set of acoustic features (representing emotions) and a set of lexical features (representing sentiments). These sets are then combined and used in a supervised classifier to predict the sentiments of customers. We use an audio speech dataset prepared from Amazon product reviews and downloaded from the YouTube portal for the purposes of our experimental evaluations.

• 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).

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.35-40
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• 2021

In this paper, we proposed a multi-subject-level sentiment analysis model for user reviews using the Latent Dirichlet Allocation (LDA) method targeting user-generated content (UGC). Data were collected from users' online reviews of hotels in major tourist cities in the world, and 30 hotel-related topics were extracted using the entire user reviews through the LDA technique. Six major hotel-related themes (Cleanliness, Location, Rooms, Service, Sleep Quality, and Value) were selected from the extracted themes, and emotions were evaluated for sentences corresponding to six themes in each user review in the proposed sentiment analysis model. Sentiment was analyzed using a dictionary. In addition, the performance of the proposed sentiment analysis model was evaluated by comparing the emotional values for each subject in the user reviews and the detailed scores evaluated by the user directly for each hotel attribute. As a result of analyzing the values of accuracy and recall of the proposed sentiment analysis model, it was analyzed that the efficiency was high.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.818-833
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• 2021

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequency-inverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model's F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.317-324
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• 2013

Twitter sentiment analysis is to classify a tweet (message) into positive and negative sentiment class. This study deals with SentiWordNet(SWN)-based twitter sentiment analysis. SWN is a sentiment dictionary in which each sense of an English word has a positive and negative sentimental strength. There has been a variety of SWN-based sentiment feature extraction methods which typically first determine the sentiment orientation (SO) of a term in a document and then decide SO of the document from such terms' SO values. For example, for SO of a term, some calculated the maximum or average of sentiment scores of its senses, and others computed the average of the difference of positive and negative sentiment scores. For SO of a document, many researchers employ the maximum or average of terms' SO values. In addition, the above procedure may be applied to the whole set (adjective, adverb, noun, and verb) of parts-of-speech or its subset. This work provides a comparative study on SWN-based sentiment feature extraction schemes with performance evaluation on a well-known twitter dataset.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.671-680
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• 2014

Recently, with the remarkable increase of social network services, it is necessary to extract interesting information from lots of data about various individual opinions and preferences on SNS(Social Network Service). The sentiment information can be applied to various fields of society such as politics, public opinions, economics, personal services and entertainments. To extract sentiment information, it is necessary to use processing techniques that store a large amount of SNS data, extract meaningful data from them, and search the sentiment information. This paper proposes an efficient method to extract sentiment information from various unstructured big data on social networks using HDFS(Hadoop Distributed File System) platform and MapReduce functions. In experiments, the proposed method collects and stacks data steadily as the number of data is increased. When the proposed functions are applied to sentiment analysis, the system keeps load balancing and the analysis results are very close to the results of manual work.