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

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

FATS: a framework for annotation of travel blogs based on subjectivity

This paper describes a framework for annotation on travel blogs based on subjectivity (FATS). The framework has the capability to auto-annotate -sentence by sentence- sections from blogs (posts) about travelling in the Spanish language. FATS is used in this experiment to annotate com- ponents from travel blogs in order to create a corpus of 300 annotated posts. Each subjective element in a sentence is annotated as positive or negative as appropriate. Currently correct annotations add up to about 95 per cent in our subset of the travel domain. By means of an iterative process of annotation we can create a subjectively annotated domain specific corpus

Detecting subjectivity through lexicon-grammar. strategies databases, rules and apps for the italian language

2014 - 2015The present research handles the detection of linguistic phenomena connected to subjectivity, emotions and opinions from a computational point of view. The necessity to quickly monitor huge quantity of semi-structured and unstructured data from the web, poses several challenges to Natural Language Processing, that must provide strategies and tools to analyze their structures from a lexical, syntactical and semantic point of views. The general aim of the Sentiment Analysis, shared with the broader fields of NLP, Data Mining, Information Extraction, etc., is the automatic extraction of value from chaos; its specific focus instead is on opinions rather than on factual information. This is the aspect that differentiates it from other computational linguistics subfields. The majority of the sentiment lexicons has been manually or automatically created for the English language; therefore, existent Italian lexicons are mostly built through the translation and adaptation of the English lexical databases, e.g. SentiWordNet and WordNet-Affect. Unlike many other Italian and English sentiment lexicons, our database SentIta, made up on the interaction of electronic dictionaries and lexicon dependent local grammars, is able to manage simple and multiword structures, that can take the shape of distributionally free structures, distributionally restricted structures and frozen structures. Moreover, differently from other lexicon-based Sentiment Analysis methods, our approach has been grounded on the solidity of the Lexicon-Grammar resources and classifications, that provides fine-grained semantic but also syntactic descriptions of the lexical entries. According with the major contribution in the Sentiment Analysis literature, we did not consider polar words in isolation. We computed they elementary sentence contexts, with the allowed transformations and, then, their interaction with contextual valence shifters, the linguistic devices that are able to modify the prior polarity of the words from SentIta, when occurring with them in the same sentences. In order to do so, we took advantage of the computational power of the finite-state technology. We formalized a set of rules that work for the intensification, downtoning and negation modeling, the modality detection and the analysis of comparative forms. With regard to the applicative part of the research, we conducted, with satisfactory results, three experiments on the same number of Sentiment Analysis subtasks: the sentiment classification of documents and sentences, the feature-based Sentiment Analysis and the Semantic Role Labeling based on sentiments. [edited by author]XIV n.s

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

A New Hybrid Approach to Sentiment Classification

With the advancement of the World Wide Web, opinion sharing online has gained a lot of popularity. These opinions are utilized for decision making, market analysis, as well as other applications. The need to harness these opinions, and the motivation behind this need has led to the development and subsequent advancement of the field of Sentiment Analysis. Various issues have arisen from these, such as difficulty in locating these opinions in a body of text, as well as determining the sentiment/polarity of these opinions. To tackle the issue of opinion polarity determination, a number of classification approaches have been developed. These approaches have focused on opinion classification at various levels, such as document, sentence and aspect levels. Most document level approaches treat documents as a bag of words during the classification process, and hence classify them as a whole. The problem with this is that there could be a mixture of opinions directed towards various aspects, within a document. It is therefore imperative to utilize a classification approach which takes into account these constituent opinions. This is the focus of classification approaches which work at the aspect level. Another important factor in the issue of sentiment/polarity classification is the choice of the classification approach. This can be machine learning, lexical/lexicon-based, and more recently, hybrid. The machine learning approaches have the benefits of carrying out classification with high accuracies, and efficiently handling large feature sets, which makes them a favourite choice where high accuracies are desired. They however also have the drawback of difficulty in adaptability, due to the domain dependency of sentiment words. The pure lexicon-based approaches do not achieve the accuracy of the machine learning approaches, but are said to offer more explainable results and take into consideration the information in lexicons. In this work, we present a novel hybrid approach, which incorporates information from lexicons in a machine learning classifier, and takes as features various linguistic knowledge sources. Our novel hybrid approach utilizes transitive dependencies to incorporate the opinions expressed towards different aspects of a document in determining the polarity classification of the whole document. The domain dependency of sentiment words is also addressed through the use of composite features and a domain specific lexicon created in this work. It was found that the use of transitive dependencies in an aspect-focused classification is a promising area, which has the potential of improving aspect based classification once the aspects have been properly determined. It was also found that although using composite features does not necessarily improve the classification accuracy, it gives rise to context rich classifiers, and the domain specific lexicon generated performed on par with the widely used generic lexicon, SentiWordNet

Sentiment Analysis in Social Streams

In this chapter, we review and discuss the state of the art on sentiment analysis in social streams—such as web forums, microblogging systems, and social networks, aiming to clarify how user opinions, affective states, and intended emo tional effects are extracted from user generated content, how they are modeled, and howthey could be finally exploited.We explainwhy sentiment analysistasks aremore difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the mainstream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities