Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs

Koto, Fajri; Rahmaningtyas, Gemala Y.

doi:10.1109/ialp.2017.8300625

Cited by 69 publications

(50 citation statements)

References 11 publications

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“…For the orthography feature, we used the number of exclamation mark, question mark, uppercase and lowercase. Meanwhile, for the lexicon features, we used sentiment lexicon (negative and positive sentiment) given by (Koto and Rahmaningtyas, 2017) and abusive lexicon that we built ourselves compiled from abusive words that used as queries when crawling Twit-ter data. After the feature extraction process was done, the dataset is ready for classification process.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Ibrohim¹,

Budi²

2019

Proceedings of the Third Workshop on Abusive Language Online

163

122

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Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflicts between citizens. Moreover, hate speech has a target, category, and level that also need to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approaches with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general the RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.

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Section: Experiments and Discussionmentioning

confidence: 99%

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Ibrohim¹,

Budi²

2019

Proceedings of the Third Workshop on Abusive Language Online

163

122

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“…The following is a description of the used lexicons, as well as the transformations we needed to apply to be able to use them with our model. First, lexicon AFINN-165-EN, (hereinafter referred to as AFINN) [45], consists of 3,382 English words to which a numeric sentiment score was manually assigned. Its score range is [-5,5].…”

Section: Selected Lexiconsmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

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“…Sentiment analysis. There has been sentiment analysis for Indonesian domains/data sources including presidential elections (Ibrahim et al, 2015), stock prices (Cakra and Trisedya, 2015), Twitter (Koto and Rahmaningtyas, 2017), and movie reviews (Nurdiansyah et al, 2018). Most previous work, however, has used non-public and low-resource datasets.…”

Section: Semantic Tasksmentioning

confidence: 99%

IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP

Koto¹,

Rahimi²,

Lau³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

Self Cite

133

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Although the Indonesian language is spoken by almost 200 million people and the 10th mostspoken language in the world, 1 it is under-represented in NLP research. Previous work on Indonesian has been hampered by a lack of annotated datasets, a sparsity of language resources, and a lack of resource standardization. In this work, we release the INDOLEM dataset comprising seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. We additionally release INDOBERT, a new pre-trained language model for Indonesian, and evaluate it over INDOLEM, in addition to benchmarking it against existing resources. Our experiments show that INDOBERT achieves state-of-the-art performance over most of the tasks in INDOLEM.

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Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs

Cited by 69 publications

References 11 publications

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter

On the Statistical and Temporal Dynamics of Sentiment Analysis

IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP

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