Sentiment Analysis: State of the Art

We present the state of art in sentiment analysis which covers the purpose of sentiment analysis, levels of sentiment analysis and processes that could be used to measure polarity and classify labels. Moreover, brief details about some resources of sentiment analysis are included

Classifying Attitude by Topic Aspect for English and Chinese Document Collections

The goal of this dissertation is to explore the design of tools to help users make sense of subjective information in English and Chinese by comparing attitudes on aspects of a topic in English and Chinese document collections. This involves two coupled challenges: topic aspect focus and attitude characterization. The topic aspect focus is specified by using information retrieval techniques to obtain documents on a topic that are of interest to a user and then allowing the user to designate a few segments of those documents to serve as examples for aspects that she wishes to see characterized. A novel feature of this work is that the examples can be drawn from documents in two languages (English and Chinese). A bilingual aspect classifier which applies monolingual and cross-language classification techniques is used to assemble automatically a large set of document segments on those same aspects. A test collection was designed for aspect classification by annotating consecutive sentences in documents from the Topic Detection and Tracking collections as aspect instances. Experiments show that classification effectiveness can often be increased by using training examples from both languages. Attitude characterization is achieved by classifiers which determine the subjectivity and polarity of document segments. Sentence attitude classification is the focus of the experiments in the dissertation because the best presently available test collection for Chinese attitude classification (the NTCIR-6 Chinese Opinion Analysis Pilot Task) is focused on sentence-level classification. A large Chinese sentiment lexicon was constructed by leveraging existing Chinese and English lexical resources, and an existing character-based approach for estimating the semantic orientation of other Chinese words was extended. A shallow linguistic analysis approach was adopted to classify the subjectivity and polarity of a sentence. Using the large sentiment lexicon with appropriate handling of negation, and leveraging sentence subjectivity density, sentence positivity and negativity, the resulting sentence attitude classifier was more effective than the best previously reported systems

Exploring the relationship between code-switching and emotions. A systematic review.

This systematic review aims to assess the interaction of code-switching (CS) and emotions, and how this relationship may be influenced by cross-cultural and cross-modality differences. The link between language preference and emotions has long been discussed, yet an overview on factors within and underlying the association is still lacking. As such, there is a critical need for a systematic evaluation of the interaction of CS and emotions. An initial search in four databases resulted in 1944 papers, which were then screened by 6 inclusion criteria. A total of 19 studies within the last 10 years were identified, with the majority of studies based in Asian contexts, and English as the most common second language. The overall risk of bias of included studies was moderate-to-high, and the key issue was the ambiguity of data collection. Narrative synthesis of selected studies showed that CS and emotions interacted with each other both in intrapersonal and interpersonal perspectives. People tended to switch their code when feeling emotional. CS was also applied for several affection functions, especially in expressing, negotiating, and managing emotions. Intensive, abrupt, and negatively charged emotions were often linked to CS, with anger being identified most frequently. This interaction could probably be either conscious or unintentional. Compared to people from western cultures, people originally from Asia were found applying CS uniquely for emotional support. No salient cross-modality difference was found. Overall, results of reviewed studies were consistent. Emotions could influence the occurrence of CS and CS was also employed for emotional functions. This interaction is influenced by personal language experiences that shaped by their social and cultural backgrounds. Further studies on other cross-linguistic and cross-cultural differences are suggested to build up the interaction to a broader context

A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

Lexical Strategies of Chinese Learners of English in L1-L2 Translation

Es handelt sich hier um eine Dissertation, die sich mit dem Gebrauch von lexikalischen Suchstrategien in der Schriftform von L1 in L2 bei chinesischen Fortgeschrittenen und Anfängern mit Vorkenntnissen als Fremdsprache beschäftigt. Die Absicht der Studie war: (a) die lexikalischen Strategien zu beschreiben und zu analysieren wie englischlernende Chinesen ein Wort oder Satz versuchen zu übersetzten, wenn es ihnen nicht möglich ist dieses Wort oder die Bedeutung einer Phrase in einem geschriebenen Text zu Formulieren. (b) zu Untersuchen ob es Beziehungen zwischen den lexikalischen Strategien und den Fertigkeiten entsprechend eines L2 Lerners gibt (c) die Effektivität von verschiedenen Strategietypen für Gruppen verschiedener Fertigkeitslevels herauszuarbeiten (d) den Vorteil von L2 benutzende Gruppen und individuellen Strategien auszuarbeiten (e) die Diskrepanz zwischen L2 profiency levels und der Übersetzungsfähigkeit und die Charakteristik des Denkens und der lexikalischen Strategien der Lernenden beim Übersetzen von L1 in L2 darzustellen. Es wird die Annahme gestellt, dass lautes Denken und retrospektive Studien als Untersuchungsmethode zur Sammlung von empirischen Daten genutzt werden können. Alle Protokolle über lautes Denken und retrospektive Daten wurden aufgezeichnet und ausgearbeitet, um die verschieden lexikalen Strategien auszuarbeiten, die englischlernende Chinesen verschiedener Fertigkeitslevels benutzen. Verschiedene Strategien über Gruppen verschiedener Fertigkeiten wurden statistisch und die Signifikanz über den Gebrauch von verschiedenen lexikalischen Strategien ausgewertet und mit Hilfe von referenzieller Statistik bewiesen. Beim Analysieren der Daten betreffend the theoretical backround of the bilingual mental lexicon ( De Bot, 1993), language transfer (Odlin, 1989; Ringbom, 1987, 1991, 2001) und communication strategies (Bialystok, 1990; Kasper & Kellerman, 1997; Tarone, 1983); diese Ergebnisse wurden in der Studie erzielt: (1) die Taxonomie der lexikalischen Strategien von englischlernenden Chinesen als Anfängern mit Vorkenntnissen und Fortgeschrittenen wurde ermittelt. (2) Fortgeschrittene englischlernende Chinesen bevorzugten L2-basierte Strategien, während Anfänger mit Vorkenntnissen Strategien aussuchten, die aus ihrer Muttersprache stammten. (3) es gibt die Möglichkeit die sowohl L1- als auch L2-Strategien zu nutzten. (4) es zeigte sich dass, die Effektivität der lexikalischen Strategien abhängig ist von „the ease of comprehension“ (Littlemore, 2003) (5) Nomen-Plus-Nomen Komposita Strukturen wurden mehr von Anfängern mit Vorkenntnissen benutzt, jedoch auch die Fortgeschrittenen bevorzugten diese Techniken, weil die chinesische Sprache sich der Komposita Struktur bedient. (6) L2 proficiency bedeutet nicht unbedingt, dass eine Übersetzung besser sein muss. Dies bedeutet nicht unbedingt, dass die L2 profiency für eine höhere Qualität steht, sondern es gibt auch andere Faktoren. Die Studie kommt zum Schluss, dass Chinesen verschiedener L2 Fähigkeiten, die englisch lernen eine Kombination von lexikalischen Strategien benutzen, wobei die Bevorzugung der lexikalischen Strategien sich unterscheiden zwischen den einzelnen Personen und deren Fertigkeiten. Die Lehrimplikationen die diese Studien auslösen, könnten behilflich sein bei der Wortsuche im geistigen Lexikon der Lernenden. So ist das Lehren von lexikalen Strategien erstrebenswert. (Zimmermann, 1999). Obgleich die Ergebnisse der Studie ein besseres Verstehen von L2 Akquisition und Bilingualismus bewirkt, ist diese Studie selbstverständlich begrenzt und benötigt weitere Studien

Learning Multilingual Subjective Language via Cross-Lingual Projections

This paper discusses learning multilingual subjective language via cross-lingual projections

Extrapolating Subjectivity Research to Other Languages

Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the implementation of techniques on par with those developed for English, and by applying methods that are lighter in the usage of text processing infrastructure, we are able to conduct multilingual subjectivity research in these languages as well. Since my aim is also to minimize the amount of manual work required to develop lexica or corpora in these languages, the techniques proposed employ a lever approach, where English often acts as the donor language (the fulcrum in a lever) and allows through a relatively minimal amount of effort to establish preliminary subjectivity research in a target language

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