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Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets
| Content Provider | Paperity |
|---|---|
| Author | Güven, Zekeriya Anıl Çakaloğlu, Tolgahan Diri, Banu |
| Abstract | Understanding the reason behind the emotions placed in the social media plays a key role to learn mood characterization of any written texts that are not seen before. Knowing how to classify the mood characterization leads this technology to be useful in a variety of fields. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise. Zemberek, Snowball, and first 5 letters root extraction methods are used to create models. The generated models were tested by using the proposed n-stage LDA method. With the proposed method, we aimed to increase model’s success rate by decreasing the number of words in the dictionary. Using the multi-stage LDA (2-stages:70.5%, 3-stages:76.375%) method, the success rate was increased compared to normal LDA (60.375%) for 5 class. |
| Starting Page | 467 |
| Ending Page | 472 |
| File Format | HTM / HTML |
| DOI | 10.21541/apjes.459447 |
| Issue Number | 7 |
| Journal | Akademik Platform Mühendislik ve Fen Bilimleri Dergisi |
| Volume Number | 3 |
| e-ISSN | 21474575 |
| Language | English |
| Publisher Date | 2019-09-28 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
