Sentiment Analysis on Turkish Social Media Shares through Lexicon Based Approach

Karamollaoglu, Hamdullah; Doğru, İbrahim Alper; Dörterler, Murat; Utku, Anıl; Yıldız, Oktay

doi:10.1109/ubmk.2018.8566481

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…In the study of Coban et al [ 17 ] in 2015, the accuracy of up to 66% was obtained by tagging tweets received on Twitter according to emojis and classifying them with various machine learning algorithms using two different feature extraction methods, Bag of words and N-gram model. In another study conducted by Karamollaoglu et al [ 18 ] in 2018, sentiment analysis processes were applied to user comments collected from various websites using the Lexicon-Based method. The classification and sentiment analysis process were carried out with an average success rate of 80%.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Balli

Güzel

Bostancı

et al. 2022

Computational Intelligence and Neuroscience

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Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to ∼87% with test data from datasets of both datasets and trained models, but also up to ∼84% with small “Sample Test Data” generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.

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Section: Literature Reviewmentioning

confidence: 99%

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Balli

Güzel

Bostancı

et al. 2022

Computational Intelligence and Neuroscience

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“…In this manner, the idea words that are far from the feature take a low score. Also, many studies have been carried out that classify posts shared in social networks as having positive, negative, or neutral sentiments 137–141 …”

Section: Semantic Analysis In Social Networkmentioning

confidence: 99%

“…Also, many studies have been carried out that classify posts shared in social networks as having positive, negative, or neutral sentiments. [137][138][139][140][141] As a major methodology of text research and applications in the SNA, semantic similarity criterion is frequently handled in online SNA. 142 This criterion evaluates the degree of semantic equivalence between two linguistic objects like concepts, sentences, or documents.…”

Section: Semantic Text Analysismentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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“…There are some limitations while translating from one language to another language like variation of slang, non-standard language, ambiguity, and thwarted expectation phenomenon. Karamollaoglu et al [33] performed sentiment analysis on Turkish messages by extracting equivalent English words for Turkish words using an online dictionary. This process leads to misinterpretation and ambiguity of sentiment conveyed in sentences.…”

Section: Introductionmentioning

confidence: 99%

Lexicon-based sentiment analysis for Kannada-English code-switch text

Chundi

Hulipalled²,

Simha³

2023

IJ-AI

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Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.

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Sentiment Analysis on Turkish Social Media Shares through Lexicon Based Approach

Cited by 9 publications

References 5 publications

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Semantic analysis on social networks: A survey

Lexicon-based sentiment analysis for Kannada-English code-switch text

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