LEKSIKON UNTUK DETEKSI EMOSI DARI TEKS BAHASA INDONESIA

Deteksi emosi dari teks merupakan bidang penelitian yang menarik perhatian beberapa tahun terakhir. Salah satu komponen utama dalam deteksi emosi adalah leksikon emosi. Makalah ini memaparkan proses pengembangan leksikon emosi untuk bahasa Indonesia. Pengembangan leksikon terdiri dari 2 proses utama yaitu pemilihan seed words dan perluasan leksikon. Pemilihan seed words dilakukan berdasarkan jenis emosi yaitu senang, cinta, marah, takut dan sedih. Jumlah seed words yang digunakan sebanyak 124 kata. Perluasan leksikon dilakukan menggunakan Tesaurus Bahasa Indonesia. Setiap kata dalam leksikon diberi bobot biner 1 atau 0. Leksikon emosi yang dihasilkan terdiri dari 1165 kata

LEKSIKON UNTUK DETEKSI EMOSI DARI TEKS BAHASA INDONESIA

Deteksi emosi dari teks merupakan bidang penelitian yang menarik perhatian beberapa tahun terakhir. Salah satu komponen utama dalam deteksi emosi adalah leksikon emosi. Makalah ini memaparkan proses pengembangan leksikon emosi untuk bahasa Indonesia. Pengembangan leksikon terdiri dari 2 proses utama yaitu pemilihan seed words dan perluasan leksikon. Pemilihan seed words dilakukan berdasarkan jenis emosi yaitu senang, cinta, marah, takut dan sedih. Jumlah seed words yang digunakan sebanyak 124 kata. Perluasan leksikon dilakukan menggunakan Tesaurus Bahasa Indonesia. Setiap kata dalam leksikon diberi bobot biner 1 atau 0. Leksikon emosi yang dihasilkan terdiri dari 1165 kata

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions

Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles