New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

Summarizing threads in blogs using opinion polarity

The huge amount of data available on the Web needs to be organized in order to be accessible to users in real time. This paper presents a method for summarizing subjective texts based on the strength of the opinion expressed in them. We used a corpus of blog posts and their corresponding comments (blog threads) in English, structured around five topics and we divided them according to their polarity and subsequently summarized. Despite the difficulties of real Web data, the results obtained are encouraging; an average of 79% of the summaries is considered to be comprehensible. Our work allows the user to obtain a summary of the most relevant opinions contained in the blog. This allows them to save time and be able to look for information easily, allowing more effective searches on the Web.

Machine learning techniques for automatic opinion detection in non-traditional textual genres

This paper presents a preliminary study in which Machine Learning experiments applied to Opinion Mining in blogs have been carried out. We created and annotated a blog corpus in Spanish using EmotiBlog. We evaluated the utility of the features labelled firstly carrying out experiments with combinations of them and secondly using the feature selection techniques, we also deal with several problems, such as the noisy character of the input texts, the small size of the training set, the granularity of the annotation scheme and the language object of our study, Spanish, with less resource than English. We obtained promising results considering that it is a preliminary study.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Social media and sentiment in bioenergy consultation

he push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable Purpose The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Originality/value Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

Big Data Analysis in Social Networks : Extracting Food Preferences of Vegans from Twitter

Market research is often conducted through conventional methods such as surveys, focus groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming. This study develops a new method, based on a combination of standard techniques like sentiment analysis and normalisation, to conduct market research in a manner that is free and quick. The method can be used in many application-areas, but this study focuses mainly on the veganism market to identify vegan food preferences in the form of a profile. Several food words are identified, along with their distribution between positive and negative sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and chicken dominate the profile, indicating that there is a significant market for vegan-suitable alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato, blueberries, kale and tofu also make strong appearances in the profile. Validation is performed by using the method on Volkswagen vehicle data to identify positive and negative sentiment across five car models. Some results were found to be consistent with sales figures and expert reviews, while others were inconsistent. The reliability of the method is therefore questionable, so the results should be used with caution.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

A comparative study of open domain and opinion question answering systems for factual and opinionated queries

The development of the Web 2.0 led to the birth of new textual genres such as blogs, reviews or forum entries. The increasing number of such texts and the highly diverse topics they discuss make blogs a rich source for analysis. This paper presents a comparative study on open domain and opinion QA systems. A collection of opinion and mixed fact-opinion questions in English is defined and two Question Answering systems are employed to retrieve the answers to these queries. The first one is generic, while the second is specific for emotions. We comparatively evaluate and analyze the systems’ results, concluding that opinion Question Answering requires the use of specific resources and methods.

EmotiBlog: a fine-grained model for emotion detection in non-traditional textual genres

This paper presents the first version of EmotiBlog, an annotation scheme for emotions in non-traditional textual genres such as blogs or forums. We collected a corpus composed by blog posts in three languages: English, Spanish and Italian and about three topics of interest. Subsequently, we annotated our collection and carried out the inter-annotator agreement and a ten-fold cross-validation evaluation, obtaining promising results. The main aim of this research is to provide a finer-grained annotation scheme and annotated data that are essential to perform evaluation focused on checking the quality of the created resources.

Improving subjectivity detection using unsupervised subjectivity word sense disambiguation

En este trabajo se presenta un método para la detección de subjetividad a nivel de oraciones basado en la desambiguación subjetiva del sentido de las palabras. Para ello se extiende un método de desambiguación semántica basado en agrupamiento de sentidos para determinar cuándo las palabras dentro de la oración están siendo utilizadas de forma subjetiva u objetiva. En nuestra propuesta se utilizan recursos semánticos anotados con valores de polaridad y emociones para determinar cuándo un sentido de una palabra puede ser considerado subjetivo u objetivo. Se presenta un estudio experimental sobre la detección de subjetividad en oraciones, en el cual se consideran las colecciones del corpus MPQA y Movie Review Dataset, así como los recursos semánticos SentiWordNet, Micro-WNOp y WordNet-Affect. Los resultados obtenidos muestran que nuestra propuesta contribuye de manera significativa en la detección de subjetividad.

ElectionMap: una representación geolocalizada de intenciones de voto hacia partidos políticos sobre la base de comentarios de usuarios de Twitter

ElectionMap es una aplicación web que realiza un seguimiento a los comentarios publicados en Twitter en relación a entidades que refieren a partidos políticos. Las opiniones de los usuarios sobre estas entidades son clasificadas según su valoración y posteriormente representadas en un mapa geográfico para conocer la aceptación social sobre agrupaciones políticas en las distintas regiones de la geografía española.

A review on political analysis and social media

En los países democráticos, conocer la intención de voto de los ciudadanos y las valoraciones de los principales partidos y líderes políticos es de gran interés tanto para los propios partidos como para los medios de comunicación y el público en general. Para ello se han utilizado tradicionalmente costosas encuestas personales. El auge de las redes sociales, principalmente Twitter, permite pensar en ellas como una alternativa barata a las encuestas. En este trabajo, revisamos la bibliografía científica más relevante en este ámbito, poniendo especial énfasis en el caso español.