Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment

Opinion mining: Reviewed from word to document level

International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

Automatically generating a sentiment lexicon for the Malay language

This paper aims to propose an automated sentiment lexicon generation model specifically designed for the Malay language. Lexicon-based Sentiment Analysis (SA) models make use of a sentiment lexicon for SA tasks, which is a linguistic resource that comprises a priori information about the sentiment properties of words. A sentiment lexicon is an indispensable resource for SA tasks. This is evident in the emergence of a large volume of research focused on the development of sentiment lexicon generation algorithms. This is not the case for low-resource languages such as Malay, for which there is a lack of research focused on this particular area. This has brought up the motivation to propose a sentiment lexicon generation algorithm for this language. WordNet Bahasa was first mapped onto the English WordNet to construct a multilingual word network. A seed set of prototypical positive and negative terms was then automatically expanded by recursively adding terms linked via WordNet’s synonymy and antonymy semantic relations. The underlying intuition is that the sentiment properties of newly added terms via these relations are preserved. A supervised classifier was employed for the word-polarity tagging task, with textual representations of the expanded seed set as features. Evaluation of the model against the General Inquirer lexicon as a benchmark demonstrates that it performs with reasonable accuracy. This paper aims to provide a foundation for further research for the Malay language in this area

A review of opinion mining and sentiment classification framework in social networks

The Web has dramatically changed the way we express opinions on certain products that we have purchased and used, or for services that we have received in the various industries. Opinions and reviews can be easily posted on the Web. such as in merchant sites, review portals, blogs, Internet forums, and much more. These data are commonly referred to as usergenerated content or user-generated media. Both the product manufacturers, as well as potential customers are very interested in this online 'word-of-mouth', as it provides product manufacturers information on their customers likes and dislikes, as well as the positive and negative comments on their products whenever available, giving them better knowledge of their products limitations and advantages over competitors; and also providing potential customers with useful and 'first-hand' information on the products and/or services to aid in their purchase decision making process. This paper discusses the existing works on opinion mining and sentiment classification of customer feedback and reviews online, and evaluates the different techniques used for the process. It focuses on thc areas covered by the evaluated papers, points out the areas that are well covered by many researchers and areas that are neglected in opinion mining and sentiment classification which are open for future research opportunity

Sentiment Analysis or Opinion Mining: A Review

Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works

A Look Inside the Translators’ Workspace: Discussions Around a Large Nursing Text Translation

Abstract: This article looks back on a large nursing textbook translation carried out by two translators in partnership. Time zone differences meant the translators worked with detailed discussion worksheets. Challenges involved in the translation of this 912-page text (the corpus) included Language and Culture Specific Challenges (LCSCs), which included SL and TL stylistic preferences, syntactical challenges, differences in ‘semantic coverage’, commissioner expectations and the need to align the Target Text with previous TL translations of standardized nursing terminologies.A review of the literature on the translation of text types, skopos and CSIs, is followed by a look inside the translators’ workspace. An examination of translation challenges found that Aixelá’s taxonomy of approaches to the translation of Culture Specific Items (CSIs) was often relevant to the translation of LCSCs. The findings of the analysis of challenges and approaches can be easily applied to translation of health-related texts in public service settings.Resumen: El presente artículo revisa la traducción de un libro texto de enfermería realizada por dos traductores  en colaboración. La diferencia horaria entre ellos llevó a los traductores a utilizar plantillas detalladas de discusión. Algunos de los desafíos presentados en la traducción de este texto de 912 páginas (el corpus) fueron los “retos específicos del idioma y la cultura” (LCSCs por sus siglas en inglés), así como las diferencias estilísticas propias de la lengua fuente y la lengua meta, las diferencias semánticas y sintácticas, las expectativas del cliente y la necesidad de mantener consistencia entre el texto meta y las traducciones previas de terminología en el área de la enfermería.Hemos realizado una mirada retrospectiva al ámbito de trabajo del traductor junto a una revisión bibliográfica acerca de la traducción de diferentes tipos de textos, la teoría de eskopo y los aspectos propios de cada cultura (CSIs). Mediante el análisis de los desafíos de la traducción hemos demostrado que, con frecuencia, en la traducción de “retos específicos del idioma y la cultura” es relevante la taxonomía de las técnicas de traducción de CSIs planteada por Aixelá. Por último, los resultados del análisis de dichos retos y enfoques pueden ser fácilmente aplicados a la traducción de textos relativos al área de la salud en el marco de los servicios públicos.