Orange: Tweet Profiler
Sumber: https://orange3-text.readthedocs.io/en/latest/widgets/tweetprofiler.html
Widget Tweet Profiler mendeteksi Ekman’s, Plutchik’s or Profile of Mood States’ emosi di tweets.
Input
Corpus: A collection of tweets (or other documents).
Output
Corpus: A corpus with information on the sentiment of each document.
Widget Tweet Profiler mengambil informasi tentang sentimen dari server untuk setiap tweet (or dokumen). Widget Tweet Profiler mengirim data ke server, dimana sebuah model akan menghitung probabilitas dan/atau skor emosi. Widget Tweet Profiler mendukung tiga (3) classification of emotion, yaitu Ekman’s, Plutchik’s and Profile of Mood States (POMS).
- Options:
- Attribute to use as content.
- Emotion classification, either Ekman’s, Plutchik’s or Profile of Mood States. Multi-class will output one most probable emotion per document, while multi-label will output values in columns per each emotion.
- The widget can output classes of emotion (categorical), probabilities (numeric), or embeddings (an emotional vector of the document).
- Commit Automatically automatically outputs the result. Alternatively, press Commit.
Contoh
Kita menggunakan dataset election-tweets-2016.tab untuk percobaan ini. Load data menggunakan widget Corpus dan sambungkan connect it to Tweet Profiler. We will use Content attribute for the analysis, Ekman’s classification of emotion with multi-class option and we will output the result as class. We will observe the results in a Box Plot. In the widget, we have selected to observe the Emotion variable, grouped by Author. This way we can see which emotion prevails by which author.
Referensi
Colnerič, Niko and Janez Demšar (2018). Emotion Recognition on Twitter: Comparative Study and Training a Unison Model. In IEEE Transactions on Affective Computing. Available online.