Polarity Lexicon Research Articles

Lexicon Creation for Financial Sentiment Analysis Using Network Embedding

In this study, we aim to construct a polarity dictionary specialized for the analysis of financial policies. Based on an idea that polarity words are likely located in the secondary proximity in the dependency network, we proposed an automatic dictionary construction method using secondary LINE (Large-scale Information Network Embedding) that is a network representation learning method to quantify relationship. The results suggested the possibility of constructing a dictionary using distributed representation by LINE. We also confirmed that a distributed representation with a property different from the distributed representation by the CBOW (Continuous Bag of Word) model was acquired and analyzed the differences between the distributed representation using LINE and the distributed representation using the CBOW model.

SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis

Deriving prior polarity lexica for sentiment analysis - where positive or negative scores are associated with words out of context - is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called 'ensemble method'). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords, a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica.

Context Specific Lexicon for Hindi Reviews

In the era of social networking, immense amount of posts, comments and tweets generated every second are increasing the size of social database. The analysis of this voluminous data is necessary for exploring the orientation of people's opinion about a particular entity. Most of the online data are in English language, but due to increase in technology and improved awareness of people, the online data available in Indian languages are gradually increasing. Sentiment analysis of English language alone is not sufficient to know the inclination of people towards an entity, other Indian language sentiment analysis is a must, their contribution is also important for us. The available sentiment classification lexicon resources like Hindi SentiWordNet are generic in nature and hence results in average sentiment classification accuracy due to contextual dependency. To improve the sentiment classification accuracy, we present an improvised lexicon resource for Hindi language for Hotel and Movie domains. The improvised polarity lexicon has been built reflecting context sensitivity and to increase coverage it has been expanded used synonyms based approach. The built polarity lexicon resource showcases an improvement in accuracy of 42% and 78% in Movie and Hotel domain, respectively, compared to the existing Hindi SentiWordNet lexicon resource.

A Unified Framework for Creating Domain Dependent Polarity Lexicons from User Generated Reviews.

The exponential increase in the explosion of Web-based user generated reviews has resulted in the emergence of Opinion Mining (OM) applications for analyzing the users’ opinions toward products, services, and policies. The polarity lexicons often play a pivotal role in the OM, indicating the positivity and negativity of a term along with the numeric score. However, the commonly available domain independent lexicons are not an optimal choice for all of the domains within the OM applications. The aforementioned is due to the fact that the polarity of a term changes from one domain to other and such lexicons do not contain the correct polarity of a term for every domain. In this work, we focus on the problem of adapting a domain dependent polarity lexicon from set of labeled user reviews and domain independent lexicon to propose a unified learning framework based on the information theory concepts that can assign the terms with correct polarity (+ive, -ive) scores. The benchmarking on three datasets (car, hotel, and drug reviews) shows that our approach improves the performance of the polarity classification by achieving higher accuracy. Moreover, using the derived domain dependent lexicon changed the polarity of terms, and the experimental results show that our approach is more effective than the base line methods.

Sentiment Analysis for Modern Standard Arabic and Colloquial

The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations, therefore many are now looking to the field of sentiment analysis. In this paper, we present a feature-based sentence level approach for Arabic sentiment analysis. Our approach is using Arabic idioms/saying phrases lexicon as a key importance for improving the detection of the sentiment polarity in Arabic sentences as well as a number of novels and rich set of linguistically motivated features contextual Intensifiers, contextual Shifter and negation handling), syntactic features for conflicting phrases which enhance the sentiment classification accuracy. Furthermore, we introduce an automatic expandable wide coverage polarity lexicon of Arabic sentiment words. The lexicon is built with gold-standard sentiment words as a seed which is manually collected and annotated and it expands and detects the sentiment orientation automatically of new sentiment words using synset aggregation technique and free online Arabic lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product reviews, etc.). The experimental results using our resources and techniques with SVM classifier indicate high performance levels, with accuracies of over 95%.

Sentiment analysis for Chinese text based on emotion degree lexicon and cognitive theories

The mass data of social media and social networks generated by users play an important role in tracking users’ sentiments and opinions online. A good polarity lexicon which can effectively improve the classification results of sentiment analysis is indispensable to analyze the user’s sentiments. Inspired by social cognitive theories, we combine basic emotion value lexicon and social evidence lexicon to improve traditional polarity lexicon. The proposed method obtains significant improvement in Chinese text sentiment analysis by using the proposed lexicon and new syntactic analysis method.

Sentiment analysis using common-sense and context information.

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

A Framework for Sentiment Analysis in Persian

Sentiment Analysis aims at extracting peoples sentiment, opinion, and appraisal from their comments in social websites. Most research efforts in the area of sentiment analysis have concentrated on English and few works considered the problem of Persian sentiment analysis. Persian is spoken by more than a hundred million speakers around the world and is the official language of Iran, Tajikistan, and Afghanistan. Persian is a challenging language for sentiment analysis and there are few resources and tools available for Persian text processing. Therefore, in the current study we first devise a Persian polarity lexicon which is a list of words associated with their sentiment polarity. Then, we review common challenges of Persian language processing such as misspelling, word spacing, stemming, and use of informal words and propose effective solutions for them. Finally, we assess the performance of the proposed method in classifying the polarity of online cell phone reviews. The results show the superiority of our approach compared to the state-of-the-art supervised machine learning methods. Furthermore, our method is more computationally efficient than existing supervised methods.

Chinese Text Sentiment Analysis Utilizing Emotion Degree Lexicon and Fuzzy Semantic Model

Text on the web has become a valuable source for mining and analyzing user opinions on any topic. Non-native English speakers heavily support the growing use of Network media especially in Chinese. Many sentiment analysis studies have shown that a polarity lexicon can effectively improve the classification consequences. Social media, where users spontaneously generated content have become important materials for tracking people's opinions and sentiments. Meanwhile, the mathematical models of fuzzy semantics have provided a formal explanation for the fuzzy nature of human language processing. This paper investigated the limitations of traditional sentiment analysis approaches and proposed an effective Chinese sentiment analysis approach based on emotion degree lexicon. Inspired by various social cognitive theories, basic emotion value lexicon and social evidence lexicon were combined to improve sentiment analysis consequences. By using the composite lexicon and fuzzy semantic model, this new sentiment analysis approach obtains significant improvement in Chinese text.

Impact of Financial News Headline and Content to Market Sentiment

Business and financial news are important resources that investors referred to when monitoring the stock performance. News brings us the latest information about the stock market. Studies have shown that business and financial news have a strong correlation with future stock performance. Business and financial news can be used to extract sentiments and opinions that may assist in the stock price predictions. In this paper, we present a sentiment analyser for financial news articles using lexicon-based approach. We utilized two most important elements of news, the headline and the content as our test data. We use polarity lexicon to distinguish between positive and negative polarity of each term in the corpus. We further investigate on how news headline will affect the sentiment analysis by adjusting the weights of the news headline and news content’s sentiment value. Three sets of experiments were carried out using headline only, content only and headline and content as test data. In the experiment, we used non-stemming tokens and stemming tokens when considering individual word found in the news article. The preliminary results are presented and discussed in this paper.

Sentiment analysis resources for Polish: a collaborative approach

With a rapid increase in the amount of information available on the web, there is a growing need for sentiment analysis. The overall objective of our ongoing research is to work out a robust method of detecting sentiment in Polish texts by means of developing relevant polarity lexical resources. The present study attempts to illuminate the process of creating a general polarity lexicon for Polish based on outputs provided by a customised Markov Random Walk algorithm, operating on an independently built semantic network for the Polish language. The customisations include a specially developed application that allows for a collaborative evaluation of the algorithm's output so that it can be used as a newest of parameters in a subsequent iteration of experiments. The results are compared with both the findings of our previous studies as well as those reported by other researchers.

Good debt or bad debt: Detecting semantic orientations in economic texts

The use of robo‐readers to analyze news texts is an emerging technology trend in computational finance. Recent research has developed sophisticated financial polarity lexicons for investigating how financial sentiments relate to future company performance. However, based on experience from fields that commonly analyze sentiment, it is well known that the overall semantic orientation of a sentence may differ from that of individual words. This article investigates how semantic orientations can be better detected in financial and economic news by accommodating the overall phrase‐structure information and domain‐specific use of language. Our three main contributions are the following: (a) a human‐annotated finance phrase bank that can be used for training and evaluating alternative models; (b) a technique to enhance financial lexicons with attributes that help to identify expected direction of events that affect sentiment; and (c) a linearized phrase‐structure model for detecting contextual semantic orientations in economic texts. The relevance of the newly added lexicon features and the benefit of using the proposed learning algorithm are demonstrated in a comparative study against general sentiment models as well as the popular word frequency models used in recent financial studies. The proposed framework is parsimonious and avoids the explosion in feature space caused by the use of conventional n‐gram features.

Construction of Chinese polarity lexicon by integration of morpheme features

Concerning the dependence on seed words amount of the traditional method based on morpheme,and the low recall rate of traditional graph-based method,the authors proposed a method which integrated the morpheme relationship of Chinese words into the graph model,and combined the synonymy of words to build Chinese polarity lexicon by a semi-supervised learning algorithm in a graph.Firstly,a morpheme model was used to weight the similarity of two Chinese words.Secondly,synonymous words and bilingual lexicon were used to build the synonymy of words.Finally,the final relation was acquired by integrating the two relations,and Label Propagation(LP) was used to run on the relation map to distinguish the polarity of the emotion words.The experimental results show that the proposed method can achieve high accuracy and recall rate,and MicroF1 can be as high as 92.8%.The dependence on seed words amount is reduced based on the fact that when the seed word amount is 100,MicroF1 can still be 84.1%.In addition,the proposed method has fast convergence.

Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews

With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the internet, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for these aspects. In this paper, we propose a novel unsupervised and domain-independent model for detecting explicit and implicit aspects in reviews for sentiment analysis. In the model, first a generalized method is proposed to learn multi-word aspects and then a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Second a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Third, two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. Finally the model employs an approach which uses explicit aspects and opinion words to identify implicit aspects. Utilizing extracted polarity lexicon, the approach maps each opinion word in the lexicon to the set of pre-extracted explicit aspects with a co-occurrence metric. The proposed model was evaluated on a collection of English product review datasets. The model does not require any labeled training data and it can be easily applied to other languages or other domains such as movie reviews. Experimental results show considerable improvements of our model over conventional techniques including unsupervised and supervised approaches.

Bootstrapping polarity classifiers with rule-based classification

In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.

Emphatic polarity in European Portuguese and beyond

This paper deals with denials. ‘Emphatic polarity’ is here approached from a well-defined perspective that focuses on the syntactic expression of reversing reactions to assertions.Three syntactic strategies to convey emphatic affirmation in the Romance languages are described and their distribution across languages elucidated, namely: the verb reduplication strategy, the sí que (AFF that) strategy, and the sentence-final sim/sí strategy. In order to account for the common traits of the structures displaying emphatic affirmation and concomitantly for their restricted cross-linguistic availability, the paper argues for the hypothesis that: (i) the functional categories C (encoding relative polarity features) and Σ (encoding absolute polarity features) are jointly involved in the syntactic expression of emphatic polarity and must be both phonologically realized in the relevant structures; (ii) verb movement in relation to the functional heads C and Σ (the topmost head of the IP domain) plus the specificities of the polarity lexicon are the main sources of variation across the Romance languages. Under this hypothesis, (i) is the unifying factor that lies behind the variation emerging from (ii). In its final section the paper briefly discusses emphatic negation. Two patterns are identified that parallel respectively the sí que pattern and the sentence-final sim/sí pattern.

CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high‐quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion‐annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.

Transfer Learning for Multiple-Domain Sentiment Analysis — Identifying Domain Dependent/Independent Word Polarity

Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence/independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain.

Mining Feature-based Opinion Expressions by Mutual Information Approach

We propose a mutual information approach to identify feature-based opinion expressions in customer reviews. Associations between opinion words and product feature categories are built by this approach. With the association set, we can identify what product feature a review unit refers to, even in the condition without explicit appearance of feature words. It can also be used to judge the semantic relatedness between a feature word and opinion words in its context. Thus it helps to decide which opinion word should contribute its polarity to the review feature. We also introduce the construction of a polarity lexicon, which is applied to identify opinion expressions in reviews. Using the approach proposed in this paper, we supply the polarity lexicon with the related product feature information. With the resource, we can get a better opinion mining result for a product review from different feature aspects.

Sentiment Analysis of Food Recipe Comments

Sentiment analysis of food recipe comments is to identify user comments about the food recipes to the positive or the negative comments. The proposed method is suitable for analysing comments or opinions about food recipes by counting the polarity words on the food domain. The benefit of this research is to help users to choose the preferred recipes from different food recipes on online food communities. To analyse food recipes, the comments of each recipe from members of the community will be collected and classified to neutral, positive or negative comments. All recipes’ comment messages are processed using text analytics and the generated polarity lexicon. Therefore, the user can gain the information to make a smart decision. The evaluation of the comment analysis shows that the accuracy of neutral and positive comment classification is about 90%. In addition, the accuracy of negative comment identification is more than 70%.