Construction Of Sentiment Lexicon Research Articles

Brand Potential User Identification Algorithm Based on Sentiment Analysis.

This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value.

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

PurposeSentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books.Design/methodology/approachFirst, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments.FindingsThis paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book.Originality/valueCompared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.

Automatically Building Financial Sentiment Lexicons While Accounting for Negation

Financial investors make trades based on available information. Previous research has proved that microblogs are a useful source for supporting stock market decisions. However, the financial domain lacks specific sentiment lexicons that could be utilized to extract the sentiment from these microblogs. In this research, we investigate automatic approaches that can be used to build financial sentiment lexicons. We introduce weighted versions of the Pointwise Mutual Information approaches to build sentiment lexicons automatically. Furthermore, existing sentiment lexicons often neglect negation while building the sentiment lexicons. In this research, we also propose two methods (Negated Word and Flip Sentiment) to extend the sentiment building approaches to take into account negation when constructing a sentiment lexicon. We build the financial sentiment lexicons by leveraging 200,000 messages from StockTwits. We evaluate the constructed financial sentiment lexicons in two different sentiment classification tasks (unsupervised and supervised). In addition, the created financial sentiment lexicons are compared with each other and with other existing sentiment lexicons. The best performing financial sentiment lexicon is built by combining our Weighted Normalized Pointwise Mutual Information approach with the Negated Word approach. It outperforms all the other sentiment lexicons in the two sentiment classification tasks. In the unsupervised sentiment classification task, it has, on average, a balanced accuracy of 69.4%, and in the supervised setting, a balanced accuracy of 75.1%. Moreover, the various sentiment classification tasks confirm that the sentiment lexicons could be improved by taking into account negation while building the sentiment lexicons. The improvement could be made by using one of the proposed methods to incorporate negation in the sentiment lexicon construction process.

A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment Lexicon

Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training-optimization process. In our scheme, the accuracy of sentiment classification is used as the optimization objective. The candidate sentiment lexicons are regarded as the individuals that need to be optimized. Then, two genetic algorithms are specially designed to adjust the values of sentiment words in lexicon. Finally, the best individual evolved in the presented genetic algorithms is selected as the sentiment lexicon. Our method only depends on some labelled texts and does not need any linguistic knowledge or prior knowledge. It provides a simple and easy way to construct a sentiment lexicon in a specific domain. Experiment results show that the proposed method has good flexibility and can generate high-quality sentiment lexicon in specific domains.

Analyzing Vocabulary Characteristics of Colloquial Style Corpus and Automatic Construction of Sentiment Lexicon

모바일 환경에서 의사소통은 SMS 문자로 이루어진다. SMS 문자에서 사용되는 어휘들은 일반적인 한국어 문어체 문장에서 사용되는 어휘들과 다른 부류의 어휘들이 사용될 것으로 예상할 수 있다. 예를 들어, 일반적인 문어체의 경우 문장의 시작이나 끝맺음이 올바르고 문장의 구성요소가 잘 갖추어졌지만, SMS 문자 말뭉치의 경우 구성요소를 생략 및 간략한 표현으로 대체하는 경우가 많다. 이러한 어휘 사용 특성을 분석하기 위하여, 기존에 구축된 구어체 말뭉치와 문어체 말뭉치를 사용한다. 실험에서는 구어체 말뭉치인 SMS 문자 말뭉치와 네이버 영화평 말뭉치, 그리고 문어체 말뭉치인 한국어 문어체 원시 말뭉치의 어휘 사용 특성을 비교-분석한다. 말뭉치별 어휘 비교 및 분석을 위하여 품사 태그 형용사(VA)를 기준으로 하였고, 공연강도를 측정하기 위해 변별적 공연어휘소 분석 방법론을 사용하였다. 그 결과 ‘좋-’, ‘죄송하-’, ‘즐겁-’ 등 감정표현 형용사들이 SMS 문자 말뭉치에서 선호되는 반면, 네이버 영화평 말뭉치에서는 평가 표현과 관련된 형용사들이 선호되는 것을 확인할 수 있었다. 이러한 과정에서 추출된 공연강도가 높은 형용사를 기준으로 감정어휘 사전을 자동 구축하기 위하여 단어 임베딩 기법을 사용하였으며, 총 343,603개의 감성어휘를 자동 구축하였다.

The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic Disambiguation

Sentiment lexicon, which provides sentiment information for words, plays an important role in sentiment analysis task. Currently, most of sentiment lexicons have only one sentiment polarity for each word and ignore sentimental ambiguity. In this paper, we propose to construct the sentiment lexicon based on context-dependent part-of-speech (POS) chunks, namely CP-chunks, which aims at solving the ambiguity of lexical sentiments. Given that the POS of context has impact on the word polarity and intensity, we take CP-chunks as an unit to do sentiment calculation. Our method is evaluated through the classification task of text sentiment. The experiment results indicate that, in comparison with the existing methods, the applicability of our method is more stable and balanced for both the positive and negative polarities corpora, and the accuracy of our method reaches 82% for the sentiment classification of a domain-specific corpus.

Sparse Self-Attention LSTM for Sentiment Lexicon Construction

Sentiment lexicon is a very important resource for opinion mining. Recently, many state-of-the-art works employ deep learning techniques to construct sentiment lexicons. In general, they firstly learn sentiment-aware word embeddings, and then use it as word features to construct sentiment lexicons. However, these methods do not consider the importance of each word to the distinguish of documents’ sentiment polarities. As we know, most words among a document do not contribute to understand documents’ semantic or sentiment. For example, in the tweet It's a good day, but i can't feel it. I'm really unhappy. The words ‘unhappy’, ‘feel’ and ‘can't’ are much more important than the words ‘good’, ‘day’ in predicting the sentiment polarity of this twitter. Meanwhile, many words, such as ‘the’, ‘in’, ‘it’ and ‘I'm’ are uninformative. In this paper, we propose a novel sparse self-attention LSTM SSALSTM to efficiently capture the above intuitive facts, and then construct a large scale sentiment lexicons in twitter. In SSALSTM, we use a novel self-attention mechanism to capture the importance of each words to the distinguish of documents’ sentiment polarities. In addition, a $L_1$ regularize is applied in the attentions which can ensure the sparsity characters that most words in a document are semantic and sentiment indistinguishable. Once we learn an efficient sentiment-aware word embedding, we train a classifier which uses sentiment-aware word embedding as features to predict the sentiment polarities of words. Extensive experiments on four publicly available datasets, SemEval 2013–2016, indicate that the sentiment lexicon generated by our proposed model achieves state-of-the-art performance on both supervised and unsupervised sentiment classification tasks.

Arabic sentiment analysis: studies, resources, and tools

To determine whether a document or a sentence expresses a positive or negative sentiment, three main approaches are commonly used: the lexicon-based approach, corpus-based approach, and a hybrid approach. The study of sentiment analysis in English has the highest number of sentiment analysis studies, while research is more limited for other languages, including Arabic and its dialects. Lexicon based approaches need annotated sentiment lexicons (containing the valence and intensity of its terms and expressions). Corpus-based sentiment analysis requires annotated sentences. One of the significant problems related to the treatment of Arabic and its dialects is the lack of these resources. We present in this survey the most recent resources and advances that have been done for Arabic sentiment analysis. This survey presents recent work (where the majority of these works are between 2015 and 2019). These works are classified by category (survey work or contribution work). For contribution work, we focus on the construction of sentiment lexicon and corpus. We also describe emergent trends related to Arabic sentiment analysis, principally associated with the use of deep learning techniques.

Automatic construction of target-specific sentiment lexicon

Sentiment lexicon plays an important role in sentiment analysis system. In most existing sentiment lexica, each sentiment word or phrase is given a sentiment label or score. However, a sentiment word may express different sentiment orientations describing different targets. It’s beneficial but challenging to incorporate knowledge of opinion targets into sentiment lexicon. In this paper we propose an automatic approach to construct a target-specific sentiment lexicon, in which each term is an opinion pair consisting of an opinion target and an opinion word. The approach solves two principle problems in construction process, namely, opinion target extraction and opinion pair sentiment classification. An unsupervised algorithm is proposed to extract opinion pairs in high quality. Both semantic feature and syntactic feature are incorporated in the algorithm, to extract opinion pairs containing correct opinion targets. A group of opinion pairs are generated and a framework is proposed to classify their sentiment polarities. Knowledge of available resources including general-purpose sentiment lexicon and thesaurus, and context knowledge including syntactic relations and sentiment information in sentences, are extracted and integrated in a unified framework to calculate sentiment scores of opinion pairs. Experimental results on product reviews datasets in different domains prove the effectiveness of our method in target-specific sentiment lexicon construction, which can improve performances of opinion target extraction and opinion pair sentiment classification. In addition, our lexicon also achieves better performance in target-level sentiment classification compared with several general-purpose sentiment lexicons.

Build a Tourism-Specific Sentiment Lexicon Via Word2vec

Online travel and online travel culture developed fast in China recently years while useful knowledge still hidden under a large number of tourism reviews. Therefore, we need effective sentiment analysis methods to mine useful knowledge which can help tourism websites make decisions and improve their travel products. Some data-driven sentiment lexicons have poor performance on sentiment polarity classification due to lack of semantic information. Thus, we propose an effective and more proper data-driven sentiment lexicon construction method incorporating manually labeled sentiment scores, semantic similarity information that is introduced by machine learning method word2vec. Experimental results demonstrate that our method improves the performance of tourism sentiment analysis significantly.

한국어 장소 리뷰를 이용한 공간 감성어 사전 구축 방법

위치 기반 서비스를 이용하여 자신이 방문한 장소에 대한 긍정 혹은 부정적 의견을 리뷰로 남기는 것이 일상화되고 있다. 실제 방문자가 작성한 장소 리뷰에 대한 감성분석 결과는 잠재적 소비자뿐 아니라 기업에게도 유용한 정보를 제공할 수 있다. 장소에 대한 감성분석을 실시하기 위해서는 감성분석의 기준이 되는 어휘에 대한 사전이 필요하다. 그러나 현재까지 장소를 표현하는 공간 감성어에 대한 사전이 구축된 바 없다. 이에 본 연구는 실제 방문자가 한국어로 작성한 장소 리뷰 데이터를 분석하여 공간 감성어 사전을 구축하는 방법을 제안하며, 여러 장소 카테고리 중 테마공원을 대상으로 공간 감성어 사전을 구축하였다. 이를 위해 자연어 처리 기법과 통계적 기법을 활용하였으며, 사전에 포함되는 공간 감성어는 감성의 극성에 대한 정보와 극성의 정도에 대한 확률점수를 포함하고 있다. 본 연구에서 구축한 공간 감성어 사전은 3개의 테이블(SSLex_SS, SSLex_single, SSLex_combi)로 구성되며, 총 219개의 어휘를 포함한다. 이를 바탕으로 트위터에서 테마공원에 대해 작성된 글을 대상으로 감성분석을 실시하였으며, 감성의 극성 분류에 대한 전체 정확도가 0.714로 산출됨에 따라 사전의 유효성을 확인할 수 있었다.

Towards building a high-quality microblog-specific Chinese sentiment lexicon

Due to the huge popularity of microblogging services, microblogs have become important sources of customer opinions. Sentiment analysis systems can provide useful knowledge to decision support systems and decision makers by aggregating and summarizing the opinions in massive microblogs automatically. The most important component of sentiment analysis systems is sentiment lexicon. However, the performance of traditional sentiment lexicons on microblog sentiment analysis is far from satisfactory, especially for Chinese. In this paper, we propose a data-driven approach to build a high-quality microblog-specific sentiment lexicon for Chinese microblog sentiment analysis system. The core of our method is a unified framework that incorporates three kinds of sentiment knowledge for sentiment lexicon construction, i.e., the word-sentiment knowledge extracted from microblogs with emoticons, the sentiment similarity knowledge extracted from words' associations among all the messages, and the prior sentiment knowledge extracted from existing sentiment lexicons. In addition, in order to improve the coverage of our sentiment lexicon, we propose an effective method to detect popular new words in microblogs, which considers not only words' distributions over texts, but also their distributions over users.The detected new words with strong sentiment are incorporated in our sentiment lexicon.We built a microblog-specific Chinese sentiment lexicon on a large microblog dataset with more than 17 million messages. Experimental results on two microblog sentiment datasets show that our microblog-specific sentiment lexicon can significantly improve the performance of microblog sentiment analysis.

Aspect term polarity detection method

Sentiment analysis in nowadays conducted mainly with specialized linguistic resources such as text corpus, sentiment lexicons and thesauruses. But the procedure of such resources creation is very time- and labor-consuming. Therefore the problem of creating the method which allows to detect polarity with minimal efforts and is based on automatically constructed sentiment lexicon is actual and important. The sentiment analysis task is required to be performed at various levels of details depending on the knowledge domain. In the article we discuss the task of aspect term polarity detection. The aspect term polarity detection method is proposed. It differs from the others by the way of automated sentiment lexicon construction based on pointwise mutual information and distributed representations of words. The results of experiments based on the corpus from international workshop SemEval-2014 are given and they show that the proposed method provides an acceptable quality in aspect term polarity detection task.

Automatic construction of domain-specific sentiment lexicon based on constrained label propagation

Domain-specific sentiment lexicon has played an important role in most practical opinion mining systems. Due to the ubiquitous domain diversity and absence of domain-specific prior knowledge, automatic construction of domain-specific sentiment lexicon has become a challenging research topic in recent years. This paper proposes a novel automatic construction strategy of domain-specific sentiment lexicon based on constrained label propagation. The candidate sentiment terms are extracted by leveraging the chunk dependency information and prior generic lexicon. The pairwise contextual and morphological constraints are defined and extracted between sentiment terms from the domain corpus, and are exploited as prior knowledge to improve the sentiment lexicon construction. The constraint propagation is applied to spread the effect of local constraints throughout the entire collection of candidate sentiment terms. The final propagated constraints are incorporated into the label propagation for the domain-specific sentiment lexicon construction. Experimental results on real-life datasets demonstrate that our approach to constrained label propagation could dramatically improve the performance of automatic construction of domain-specific sentiment lexicon.

Graph-Based Natural Language Processing and Information Retrieval Rada Mihalcea and Dragomir Radev (University of North Texas and University of Michigan) Cambridge, UK: Cambridge University Press, 2011, viii+192 pp; hardbound, ISBN 978-0-521-89613-9, $65.00

<b>Graph-Based Natural Language Processing and Information Retrieval Rada Mihalcea and Dragomir Radev</b> (University of North Texas and University of Michigan) Cambridge, UK: Cambridge University Press, 2011, viii+192 pp; hardbound, ISBN 978-0-521-89613-9, $65.00