Sentiment Analysis Using Learning Approaches Over Emojis for Turkish Tweets

Velioğlu, Rıza; Yıldız, Tuğba; Yıldırım, Savaş

doi:10.1109/ubmk.2018.8566260

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…Considering that similar studies in the literature reported f-score values of 0.78 -0.92 for two-class sentiment analyses and 0.59 -0.78 for three-class sentiment analyses, it can be said that the results are quite successful (Çoban et al, 2015;Kaya et al, 2012;Kaynar et al, 2016;Velioğlu et al, 2018;Yıldırım et al, 2015). While the findings suggest that the sentiment analysis model created here is feasible for Turkish touristic site reviews, using the data sets in further research and comparing the findings will result in better interpretations.…”

Section: Discussionmentioning

confidence: 84%

Analysis of Turkish Sentiment Expressions About Touristic Sites Using Machine Learning

Aksu¹,

Karaman²

2021

Journal of Intelligent Systems: Theory and Applications

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Analyzing data by inferring from unstructured data about customers is one of the main purposes of the tourism and many other industries as well. However, performing unstructured data analysis using traditional methods is quite inconvenient and costly. This can be overcome by using sentiment analysis, an area of application of text mining. Since there is no proven methodology for sentiment analysis, researchers often perform their studies by trial and error. Many studies on sentiment analysis have focused on comparing the preprocessing or the performance of various machine learning algorithms. Both for these reasons and since research on sentiment analysis with Turkish content is limited, this study aimed to determine the effects of labeling, stemming, and negation on the success of sentiment analysis using Turkish touristic site analysis. From the data set prepared for this study, 12 different variations were created according to labeling, number of classes, stemming, and negation. These data sets were classified using the algorithms Naive Bayes (NB), Multinominal Naive Bayes (MNB), k-Nearest Neighbor, and Support Vector Machines (SVM), often used in sentiment analyses, and the findings were compared.

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Section: Discussionmentioning

confidence: 84%

Analysis of Turkish Sentiment Expressions About Touristic Sites Using Machine Learning

Aksu¹,

Karaman²

2021

Journal of Intelligent Systems: Theory and Applications

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“…Feature extraction technique Manoj Sethi [2], Yuwen Lyu [12], Nafiz Al Asad [13], Subhan Tariq [18], Kashif Ayyab [43], Hay Mar Su Aung [74], Alex M. G. Almeida [77], Govin Gaikwad [79], Tianyi Wang [83], Masum Billah [94], Rinki Chatterjee [95] Term Frequency Inverse Document Frequency (TF-IDF) Ganzalo A. Ruz [4], Li Chen Cheng [11], Guozheng Rao [19], Rıza Velioglu [97], Md. Mokhlesur Rahmana [100] Bag of Words (BOW) Ganzalo A. Ruz [4], Li Chen Cheng [11], Md.…”

Section: Papersmentioning

confidence: 99%

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

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“…In literature, the datasets were commonly annotated based on either using a set of keywords 21,36,37 or emoticons. [38][39][40] Unlike these works, a lexicon-based approach was employed for generalization. When it comes to the state-of-the-art lexicons, TextBlob 41 was intentionally not opted since it does not utilize social media-specific symbols such as emojis, and emoticons while analyzing the sentiment of a given sentence.…”

Section: Dataset Annotationmentioning

confidence: 99%

A novel COVID‐19 sentiment analysis in Turkish based on the combination of convolutional neural network and bidirectional long‐short term memory on Twitter

Kabakuş

2022

Concurrency and Computation

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The whole world has been experiencing the COVID‐19 pandemic since December 2019. During the pandemic, a new life has been started by necessity where people have extensively used social media to express their feelings, and find information. Twitter was used as the source of what people have shared regarding the COVID‐19 pandemic. Sentiment analysis deals with the extraction of the sentiment of a given text. Most of the related works deal with sentiment analysis in English, while studies for Turkish sentiment analysis lack in the research field. To this end, a novel sentiment analysis model based on the combination of convolutional neural network and bidirectional long short‐term memory was proposed in this study. The proposed deep neural network model was trained on the constructed Twitter dataset, which consists of 15k Turkish tweets regarding the COVID‐19 pandemic, to classify a given tweet into three sentiment classes, namely, (i) positive, (ii) negative, and (iii) neutral. A set of experiments were conducted for the evaluation of the proposed model. According to the experimental result, the proposed model obtained an accuracy as high as 97.895%, which outperformed the state‐of‐the‐art baseline models for sentiment analysis of tweets in Turkish.

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Sentiment Analysis Using Learning Approaches Over Emojis for Turkish Tweets

Cited by 20 publications

References 9 publications

Analysis of Turkish Sentiment Expressions About Touristic Sites Using Machine Learning

Analysis of Turkish Sentiment Expressions About Touristic Sites Using Machine Learning

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

A novel COVID‐19 sentiment analysis in Turkish based on the combination of convolutional neural network and bidirectional long‐short term memory on Twitter

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