A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention

The Web has become the main platform where people express their opinions about entities of interest and their associated aspects. Aspect-Based Sentiment Analysis (ABSA) aims to automatically compute the sentiment towards these aspects from opinionated text. In this paper we extend the state-of-the-art Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two directions. First we replace the non-contextual word embeddings with deep contextual word embeddings in order to better cope with the word semantics in a given text. Second, we use hierarchical attention by adding an extra attention layer to the HAABSA high-level representations in order to increase the method flexibility in modeling the input data. Using two standard datasets (SemEval 2015 and SemEval 2016) we show that the proposed extensions improve the accuracy of the built model for ABSA.Comment: Accepted for publication in the 20th International Conference on Web Engineering (ICWE 2020), Helsinki Finland, 9-12 June 202

SENTIMENT ANALYSIS FOR SEARCH ENGINE

The chief purpose of this study is to detect and eliminate the sentiment bias in a search engine. Sentiment bias means a bias induced in the search results based on the sentiment of the user’s search query. As people increasing depend on search engines for information, it is important to understand the quality of results produced by the search engines. This study does not try to build a search engine but leverage the existing search engines to provide better results to the user. In this study, only the queries that have high sentiment polarity are analyzed and the machine learning models are used to predict the sentiment polarity of the input query, sentiment polarity of the documents produced by the search engine for the given query and also to change the sentiment polarity of the input query to its opposite sentiment. This project proposes an end-to-end system that eliminates the search engine bias by producing results that align with the query sentiment as well as the opposite sentiment. The system comprising of three models for document level sentiment analysis, aspect level sentiment analysis and sentiment style transfer. The document level sentiment analyzer is an LSTM based model that uses GloVe word embeddings to analyze the sentiment of the documents produced by the search engine. The aspect level sentiment analyzer uses deep memory network with attention and auxiliary memory to analyze the sentiment of each search query. In order to obtain the iv documents of the opposite polarity, the sentiment of the search query is reversed using the sentiment style transfer model that uses a bi-directional LSTM. The results are analyzed to determine the sentiment bias of the search engine based on the input query. In our experiments, we observed that positive sentiment queries yielded 67% documents with positive sentiment and negative sentiment queries yielded 70% documents with negative sentiment. The proposed system eliminates this bias by providing the users with two sets of result, one with positive sentiment and one with negative sentiment

SentiTur: Building Linguistic Resources for Aspect-Based Sentiment Analysis in the Tourism Sector

The use of linguistic resources beyond the scope of language studies, i.e. commercial purposes, has become commonplace since the availability of massive amounts of data and the development of tools to process them. An interesting focus on these materials is provided by Sentiment Analysis (SA) tools and methodologies, which attempt to identify the polarity or semantic orientation of a text, i.e., its positive, negative, or neutral value. Two main approaches have been made in this sense, one based on complex machine-learning algorithms and the other relying principally on lexical knowledge (Taboada et al., 2011). Lingmotif is an example of lexicon-based SA tool offering polarity classification and other related metrics, together with an analysis of the target segments evaluated (Moreno-Ortiz, 2017). Sentiment has been shown to be domain-specific to a large extent (Choi & Cardie, 2008) and it is therefore necessary to study and describe how sentiment is expressed not only in general language, but also in specialized domains. The availability of annotated, domain-specific corpora could greatly enhance the capacity of SA tools. Furthermore, the demand for a more fine-grained approach requires the identification of specific domain terminology, allowing the recognition of target terms associated with the polarity (Liu, 2012). Most available SA corpora are annotated at the document level, which allows systems to be trained to return the overall orientation of the text. However, more detail is necessary: what aspects exactly are being praised or criticized? This type SA is known as Aspect-Based Sentiment Analysis (ABSA), and attempts to extract more fined-grained knowledge. ABSA has attracted the attention of recent SemEval shared-tasks (Pontiki et al., 2015)

Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

In recent years, more and more people express their feelings through both images and texts, boosting the growth of multimodal data. Multimodal data contains richer semantics and is more conducive to judging the real emotions of people. To fully learn the features of every single modality and integrate modal information, this paper proposes a fine-grained multimodal sentiment analysis method FCLAG based on gating and attention mechanism. First, the method is carried out from the character level and the word level in the text aspect. CNN is used to extract more fine-grained emotional information from characters, and the attention mechanism is used to improve the expressiveness of the keywords. In terms of images, a gating mechanism is added to control the flow of image information between networks. The images and text vectors represent the original data collectively. Then the bidirectional LSTM is used to complete further learning, which enhances the information interaction capability between the modalities. Finally, put the multimodal feature expression into the classifier. This method is verified on a self-built image and text dataset. The experimental results show that compared with other sentiment classification models, this method has greater improvement in accuracy and F1 score and it can effectively improve the performance of multimodal sentiment analysis

Cross-Cultural Examination on Content Bias and Helpfulness of Online Reviews: Sentiment Balance at the Aspect Level for a Subjective Good

Online reviews can be fraught with biases, especially on experience goods. Using multilingual sentiment analysis software, we examined the characteristics of review biases and helpfulness at the aspect level across two different cultures. First, we found the lopsidedness of emotions expressed over the four key aspects of Japanese restaurant reviews between Japanese and Western consumers. Second, helpful reviews have sentiments expressed more evenly over those aspects than average for both Japanese and Western consumers. Third, however, there are significant differences over how sentiments are spread over aspects between them. Westerners found reviews helpful when reviews focused less on food and more on service. In addition, Japanese customers were more concerned with savings whereas Westerners paid attention to whether they are getting their money’s worth. These findings point to future research opportunities for leveraging sentiment analysis over key aspects of goods, particularly those of experience/subjective goods, across different cultures and customer profile categories

Latent dirichlet markov allocation for sentiment analysis

In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model

Analisis Sentimen Level Aspek Teks Bahasa Indonesia Pada Media Sosial Menggunakan Hierarchical Attention Position-Aware Network (Studi Kasus : Politik)

Jumlah pengguna internet penduduk indonesia dari tahun ke tahun semakin meningkat. Pengunaan internet ini sebagian besar digunakan untuk berinteraksi di media sosial. Pengguna media sosial seringkali mengungkapkan pikiran atau pandangannya di media sosial salah satunya dibidang politik. Khususnya pada tahun 2019 ini dimana terjadi pesta demokrasi di Indonesia yaitu diadakannya pemilu . Hal ini membuat banyak bermunculan opini-opini tentang objek politik. Untuk mengetahui pandangan atau penilaian masyarakat terhadap suatu objek politik dapat digunakan analisis sentimen. Umumnya terdapat dua macam sentimen atau opini yang ditulis yaitu sentimen positif dan sentimen negatif. Tetapi perlu diperhatikan juga bahwa dalam satu kalimat opini dapat mengandung berbagai sentimen. Hal ini dikarenakan suatu objek politik dapat memilik berbagai aspek yang juga diberikan pandangan sendiri oleh penulis. Banyaknya jumlah opini yang tidak sesuai dengan kaidah standar akan membuat penarikan kesimpulan sentimen level aspek dari objek menjadi sulit. Sehingga diperlukan sebuah sistem yang mampu melakukan klasisfikasi sentimen level aspek terhadap objek. Klasifikasi sentimen level aspek dilakukan dengan menggunakan metode deep-learning yaitu Recurrent Neural Network dan juga berdasarkan attention. Penelitian sebelumnya melakukan percobaan dengan menggunakan metode Hierarchical Attention Position-aware network yang berdasarkan pada Bi-GRU mencapai hasil yang sangat baik. Teks yang digunakan sebagai input pelatihan model dan diubah menjadi representasi vector menggunakan metode Word Embedding. Kemudian dilakukan evaluasi performa dari model HAPN. Hasil dari penelitian tugas akhir ini adalah didapatkannya hasil klasifikasi sentimen level aspek pada objek politik yang lebih baik. ============================================================================== The number of Indonesian internet users has increased from year to year. Internet is mostly used to interact on social media. Social media users often express their thoughts or views in social media where one of them is political field. Especially in 2019 where a democratic party takes place in Indonesia, namely the holding of elections. This makes a lot of opinions on political objects emerge. To find out the views or assessments of the public about a political object, sentiment analysis can be used. Generally there are two kinds of sentiments or opinions written, namely positive sentiment and negative sentiment. But it should also be noted that in one sentence opinion can contain various sentiments. This is because a political object can have various aspects which are also given their own views by the author. The large number of opinions that are not in accordance with the standard rules will make the conclusion of the sentiment level aspect of the object becomes difficult. So we need a system that capable of classifying sentiment level aspects of objects. Aspect level sentiment classification is done by using deep-learning methods namely Recurrent Neural Network and also based on attention. Previous studies conducted experiments using the Hierarchical Attention Position-aware network (HAPN) method based on Bi-GRU achieved very good results. The text is used as an input training model and is converted to a vector representation using the Word Embedding method. Then evaluate the performance of the HAPN model. The results of this thesis research is to obtain better results from the aspect level sentiment classification on political objects