• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.97-106
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• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• Journal of Contemporary Eastern Asia
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• v.20 no.2
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• pp.108-128
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• 2021

Research on the news coverage of North Korea has been paying less attention to social media platforms than to legacy media. An increasing number of social media users post, retweet, share, interpret, and set agendas on North Korea. The accessibility of international users and North Korea's publicity purposes make social media a venue for expression, news diversity, and framing about the nation. This study examined the sentiment of Twitter posts on North Korea from a framing perspective and the relationship between network strengths and sentiment from a social network perspective. Data were collected using two tools: Jupyter Notebook with Python 3.6 for preliminary analysis and NodeXL for main analysis. A total of 11,957 tweets, 10,000 of which were collected using Python and 1,957 tweets using NodeXL, about North Korea between June 20-21, 2020 were collected. Results demonstrated that there was more negative sentiment than positive sentiment about North Korea in the sampled Twitter posts. Some users belonging to small network sizes reached out to others on Twitter to build networks and spread positive information about North Korea. Influential users tended to be impartial to sentiment about North Korea, while some Twitter users with a small network exhibited high percentages of positive words about North Korea. Overall, marginalized populations with network bonding were more likely to express positive sentiment about North Korea than were influencers at the center of networks.

• Journal of Korea Multimedia Society
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• v.19 no.7
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• pp.1107-1115
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• 2016

Efficient Market Hypothesis (EMH), states that at any point in time in a liquid market security prices fully reflect all available information. This paper presents a study of proving the hypothesis through daily Twitter sentiments using the hybrid approach of the lexicon-based approach and the naïve Bayes classifier. In this research we analyze the currency exchange rate movement of Indonesia Rupiah vs US dollar as a way of testing the Efficient Market Hypothesis. In order to find a correlation between the prediction sentiments from Twitter data and the actual currency exchange rate trends we collect Twitter data every day and compute the overall sentiment to label them as positive or negative. Experimental results have shown 69% correct prediction of sentiment analysis and 65.7% correlation with positive sentiments. This implies that EMH is semi-strong Efficient Market Hypothesis, and that public information provide by Twitter sentiment correlate with changes in the exchange market trends.

• Journal of Information Science Theory and Practice
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• v.9 no.1
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• pp.35-53
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• 2021

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter. It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time. In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed. Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic. Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change. In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries. Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels. Our intention was to analyze how the sentiment differs between countries and in response to certain events. The performance of the topic model was assessed by being compared with other alternative topic modeling techniques. The topic coherence was measured for the different techniques while changing the number of topics. Results showed that the pooling by author before performing LDA significantly improved the produced topic models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2996-3011
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• 2017

With rapid growth of web technology and dissemination of smart devices, social networking service(SNS) is widely used. As a result, huge amount of data are generated from SNS such as Twitter, and sentiment analysis of SNS data is very important for various applications and services. In the existing sentiment analysis based on the $Na{\ddot{i}}ve$ Bayes algorithm, a same number of attributes is usually employed to estimate the weight of each class. Moreover, uncountable and meaningless attributes are included. This results in decreased accuracy of sentiment analysis. In this paper two methods are proposed to resolve these issues, which reflect the difference of the number of positive words and negative words in calculating the weights, and eliminate insignificant words in the feature selection step using Multinomial $Na{\ddot{i}}ve$ Bayes(MNB) algorithm. Performance comparison demonstrates that the proposed scheme significantly increases the accuracy compared to the existing Multivariate Bernoulli $Na{\ddot{i}}ve$ Bayes(BNB) algorithm and MNB scheme.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.377-384
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• 2016

Our goal is to build the system which collects tweets from Twitter, analyzes the sentiment of each tweet, and helps users build a sentiment tagged corpus semi-automatically. After collecting tweets with the Twitter API, we analyzes the sentiments of them with a sentiment dictionary. With the proposed system, users can verify the results of the system and can insert new sentimental words or dependency relations where sentiment information exist. Sentiment information is tagged with the JSON structure which is useful for building or accessing the corpus. With a test set, the system shows about 76% on the accuracy in analysing the sentiments of sentences as positive, neutral, or negative.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.81-91
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• 2019

Recently, social network service (SNS) is actively used by public. Among them, Twitter has a lot of tweets including sentiment and it is convenient to collect data through open Aplication Programming Interface (API). In this paper, we analyze social issues and suggest the possibility of using them in marketing through sentimental information of users. In this paper, we collect twitter text about social issues and classify as positive or negative by sentiment classifier to provide qualitative analysis. We provide a quantitative analysis by analyzing the correlation between the number of like and retweet of each tweet. As a result of the qualitative analysis, we suggest solutions to attract the interest of the public or consumers. As a result of the quantitative analysis, we conclude that the positive tweet should be brief to attract the users' attention on the Twitter. As future work, we will continue to analyze various social issues.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.69-74
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• 2012

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.