A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language

ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages

The file attached to this record is the author's final peer reviewed version.A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1-score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results respectively represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature

BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate clues and sourcetarget interactions at multiple levels of granularity into bilingual phrase representations. We employ recursive autoencoders to generate tree structures of phrases with embeddings at different levels of granularity (e.g., words, sub-phrases and phrases). Over these embeddings on the source and target side, we introduce a bidimensional attention network to learn their interactions encoded in a bidimensional attention matrix, from which we extract two soft attention weight distributions simultaneously. These weight distributions enable BattRAE to generate compositive phrase representations via convolution. Based on the learned phrase representations, we further use a bilinear neural model, trained via a max-margin method, to measure bilingual semantic similarity. To evaluate the effectiveness of BattRAE, we incorporate this semantic similarity as an additional feature into a state-of-the-art SMT system. Extensive experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.63 BLEU points on average over the baseline.Comment: 7 pages, accepted by AAAI 201

MELex: a new lexicon for sentiment analysis in mining public opinion of Malaysia affordable housing projects

Sentiment analysis has the potential as an analytical tool to understand the preferences of the public. It has become one of the most active and progressively popular areas in information retrieval and text mining. However, in the Malaysia context, the sentiment analysis is still limited due to the lack of sentiment lexicon. Thus, the focus of this study is to a new lexicon and enhance the classification accuracy of sentiment analysis in mining public opinion for Malaysia affordable housing project. The new lexicon for sentiment analysis is constructed by using a bilingual and domain-specific sentiment lexicon approach. A detailed review of existing approaches has been conducted and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. The developed approach is able to analyze text for two most widely used languages in Malaysia, Malay and English, with better accuracy. The process of constructing MELex involves three activities: seed words selection, polarity assignment and synonym expansions, with four different experiments have been implemented. It is evaluated based on the experimentation and case study approaches where PR1MA and PPAM are selected as case projects. Based on the comparative results over 2,230 testing data, the study reveals that the classification using MELex outperforms the existing approaches with the accuracy achieved for PR1MA and PPAM projects are 90.02% and 89.17%, respectively. This indicates the capabilities of MELex in classifying public sentiment towards PRIMA and PPAM housing projects. The study has shown promising and better results in property domain as compared to the previous research. Hence, the lexicon-based approach implemented in this study can reflect the reliability of the sentiment lexicon in classifying public sentiments

Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201