Sentiment analysis on twitter for the portuguese language

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaWith the growth and popularity of the internet and more specifically of social networks, users can more easily share their thoughts, insights and experiences with others. Messages shared via social networks provide useful information for several applications, such as monitoring specific targets for sentiment or comparing the public sentiment on several targets, avoiding the traditional marketing research method with the use of surveys to explicitly get the public opinion. To extract information from the large amounts of messages that are shared, it is best to use an automated program to process these messages. Sentiment analysis is an automated process to determine the sentiment expressed in natural language in text. Sentiment is a broad term, but here we are focussed in opinions and emotions that are expressed in text. Nowadays, out of the existing social network websites, Twitter is considered the best one for this kind of analysis. Twitter allows users to share their opinion on several topics and entities, by means of short messages. The messages may be malformed and contain spelling errors, therefore some treatment of the text may be necessary before the analysis, such as spell checks. To know what the message is focusing on it is necessary to find these entities on the text such as people, locations, organizations, products, etc. and then analyse the rest of the text and obtain what is said about that specific entity. With the analysis of several messages, we can have a general idea on what the public thinks regarding many different entities. It is our goal to extract as much information concerning different entities from tweets in the Portuguese language. Here it is shown different techniques that may be used as well as examples and results on state-of-the-art related work. Using a semantic approach, from these messages we were able to find and extract named entities and assigning sentiment values for each found entity, producing a complete tool competitive with existing solutions. The sentiment classification and assigning to entities is based on the grammatical construction of the message. These results are then used to be viewed by the user in real time or stored to be viewed latter. This analysis provides ways to view and compare the public sentiment regarding these entities, showing the favourite brands, companies and people, as well as showing the growth of the sentiment over time

Learning to Learn from Weak Supervision by Full Supervision

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.Comment: Accepted at NIPS Workshop on Meta-Learning (MetaLearn 2017), Long Beach, CA, US

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Sentiment analysis in tweets

Orientador: Jacques WainerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Análise do sentimento é um campo de estudo de recente popularização devido ao crescimento da Internet e ao conteúdo gerado por seus usuários. Mais recentemente, as redes sociais surgiram, nessas redes as pessoas publicam suas opiniões em linguagem coloquial e compacta. Isto é o que acontece, por exemplo, no Twitter, uma ferramenta de comunicação que pode ser facilmente utilizada como fonte de informação para várias ferramentas automatizadas de inferência de sentimento. Esforços de pesquisa foram direcionados para lidar com o problema da análise do sentimento nas redes sociais do ponto de vista de um problema de classificação, onde não há consenso sobre qual é o melhor classificador, qual a melhor forma de pré- processamento entre outros. O objetivo desta dissertação é investigar a influência de algumas técnicas de pré-processamento, da técnica TF-IDF, do volume do conjunto de treinamento e de técnicas ensembles na acurácia de alguns classificadores supervisionadosAbstract: Sentiment analysis is a field of study that shows recent popularization due to the growth of Internet and the content that is generated by its users. More recently, social networks have emerged, where people post their opinions in colloquial and compact language. This is what happens in Twitter, a communication tool that can easily be used as a source of information for various automatic tools of sentiment inference. Research efforts have been directed to deal with the problem of sentiment analysis in social networks from the point of view of a classification problem, where there is no consensus about what the best classifier is, and what is the best configuration provided by the feature engineering process. The objective of this dissertation is to investigate the influence of some pre-processing techniques, the TF-IDF technique, the volume of the training set and ensembles techniques in the accuracy of some supervised techniquesMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Fidelity-Weighted Learning

Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.Comment: Published as a conference paper at ICLR 201

Identifying and Processing Crisis Information from Social Media

Social media platforms play a crucial role in how people communicate, particularly during crisis situations such as natural disasters. People share and disseminate information on social media platforms that relates to updates, alerts, rescue and relief requests among other crisis relevant information. Hurricane Harvey and Hurricane Sandy saw over tens of millions of posts getting generated, on Twitter, in a short span of time. The ambit of such posts spreads across a wide range such as personal and official communications, and citizen sensing, to mention a few. This makes social media platforms a source of vital information to different stakeholders in crisis situations such as impacted communities, relief agencies, and civic authorities. However, the overwhelming volume of data generated during such times, makes it impossible to manually identify information relevant to crisis. Additionally, a large portion of posts in voluminous streams is not relevant or bears minimal relevance to crisis situations. This has steered much research towards exploring methods that can automatically identify crisis relevant information from voluminous streams of data during such scenarios. However, the problem of identifying crisis relevant information from social media platforms, such as Twitter, is not trivial given the nature of unstructured text such as short text length and syntactic variations among other challenges. A key objective, while creating automatic crisis relevancy classification systems, is to make them adaptable to a wide range of crisis types and languages. Many related approaches rely on statistical features which are quantifiable properties and linguistic properties of the text. A general approach is to train the classification model on labelled data acquired from crisis events and evaluate on other crisis events. A key aspect missing from explored literature is the validity of crisis relevancy classification models when applied to data from unseen types of crisis events and languages. For instance, how would the accuracy of a crisis relevancy classification model, trained on earthquake type of events, change when applied to flood type of events. Or, how would a model perform when trained on crisis data in English but applied to data in Italian. This thesis investigates these problems from a semantics perspective, where the challenges posed by diverse types of crisis and language variations are seen as the problems that can be tackled by enriching the data semantically. The use of knowledge bases such as DBpedia, BabelNet, and Wikipedia, for semantic enrichment of data in text classification problems has often been studied. Semantic enrichment of data through entity linking and expansion of context via knowledge bases can take advantage of connections between different concepts and thus enhance contextual coherency across crisis types and languages. Several previous works have focused on similar problems and proposed approaches using statistical features and/or non-semantic features. The use of semantics extracted through knowledge graphs has remained unexplored in building crisis relevancy classifiers that are adaptive to varying crisis types and multilingual data. Experiments conducted in this thesis consider data from Twitter, a micro-blogging social media platform, and analyse multiple aspects of crisis data classification. The results obtained through various analyses in this thesis demonstrate the value of semantic enrichment of text through knowledge graphs in improving the adaptability of crisis relevancy classifiers across crisis types and languages, in comparison to statistical features as often used in much of the related work

EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop

Editor of the proceedings of EVALITA 2016