Adapting sentiment lexicons using contextual semantics for sentiment analysis of twitter

Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words’ sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure. © 2014 Springer International Publishing.

Mainstream media behavior analysis on Twitter:a case study on UK general election

With the development of social media tools such as Facebook and Twitter, mainstream media organizations including newspapers and TV media have played an active role in engaging with their audience and strengthening their influence on the recently emerged platforms. In this paper, we analyze the behavior of mainstream media on Twitter and study how they exert their influence to shape public opinion during the UK's 2010 General Election. We first propose an empirical measure to quantify mainstream media bias based on sentiment analysis and show that it correlates better with the actual political bias in the UK media than the pure quantitative measures based on media coverage of various political parties. We then compare the information diffusion patterns from different categories of sources. We found that while mainstream media is good at seeding prominent information cascades, its role in shaping public opinion is being challenged by journalists since tweets from them are more likely to be retweeted and they spread faster and have longer lifespan compared to tweets from mainstream media. Moreover, the political bias of the journalists is a good indicator of the actual election results. Copyright 2013 ACM.

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space

Social media in emergency management:Twitter as a tool for communicating risks to the public

One of the main challenges of emergency management lies in communicating risks to the public. On some occasions, risk communicators might seek to increase awareness over emerging risks, while on others the aim might be to avoid escalation of public reactions. Social media accounts offer an opportunity to rapidly distribute critical information and in doing so to mitigate the impact of emergencies by influencing public reactions. This article draws on theories of risk and emergency communication in order to consider the impact of Twitter as a tool for communicating risks to the public. We analyse 10,020 Twitter messages posted by the official accounts of UK local government authorities (councils) in the context of two major emergencies: the heavy snow of December 2010 and the riots of August 2011. Twitter was used in a variety of ways to communicate and manage associated risks including messages to provide official updates, encourage protective behaviour, increase awareness and guide public attention to mitigating actions. We discuss the importance of social media as means of increasing confidence in emergency management institutions.

O canto dos vestibulandos em 140 caracteres: linguagem e construção de identidades no Twitter

The revolution caused by the internet and its various social networks eventually bring forth fruitful reflections on cyberculture and the power of identity construction. What seemed purely fashion has become way of being, representation of self, reality creation (Lévy, 1996). Considering language as a social phenomenon, which occurs through interaction, as explicit in Bakhtin (1929), the speech aired on social networks shapes the profile of their users, constructing identities which, according to Hall (2006), are multiple and non-permanent . This research seeks to examine the use of Twitter by school students, developing a reflection on the construction of their own identities in cyberspace. The subjects are students of Educandário Nossa Senhora das Vitórias, private school in Assú/RN, all graduates from high school. Understanding the Vestibular year as a decisive and a reflection engine ever present about their condition of students, subjects eventually express their anxieties, fears and perspectives in the virtual environment, providing us with enough material to analyze how they are high school students, expectations for appropriate selection processes, plus several representations belonging to the school environment. From the discourse conveyed on Twitter expressed in Featured posts, this study reveals the identities of high school students that emerge from it, which led the cast of some evidence. From them, despite the multiplicity of identities observed, presented some common aspects that corroborate the requirements provided for specific objectives, such as: feeling of belonging to a group - class and school; change of routine and behavior towards education; desecration of traditional teaching practices; changing the identity of students'writings. The analysis of postings enables us to know the perceptions of students regarding the school, the disciplines , the pace of studies, interest in school practices, and from such evidence, the perception of how vestibular modify your daily life and a fondness their identities as school students.

Marketing político e mídias sociais: as estratégias utilizadas no facebook e twitter de Cássio Cunha Lima PSDB/PB, nas eleições de 2014

This dissertation aims to analyze and understand the process and practices of political marketing strategies applied to social media facebook and twitter Cássio Cunha Lima - PSDB candidate for governor of Paraíba, in the 2014 elections The work is divided into three parts . The first two chapters, both of theoretical nature, underlie the discussion about the use of the Internet as a campaign space and political marketing campaign as well as the different communication strategies and electoral marketing already presented in the literature. Following, is dedicated to a topic for the presentation of the methodology and subsequently makes the discussion of empirical data analysis. Finally, we present the conclusions. The analysis takes as its starting point the models Figueiredo et al. (1998) and Albuquerque (1999) to observe the traditional strategies and suggests the inclusion of typically recorded on the Internet strategies. The methodology used for the analysis was the qualitative and quantitative content from variables that we list different campaign strategies. In order to achieve the purpose of this research, we conducted a case study as an analytical object online campaign Cássio Cunha Lima. The case study took place from the construction of a candidate's biographical and political profile, presented and discussed in the text. This research also made use of virtual ethnography. Therefore, were monitored social media facebook and twitter that political, with the help of image capture program - Greenshot by creating pre-defined categories of analysis, for example, calendar, prestige and support, negative campaign , engagement, among others. The period chosen for monitoring the candidate's official profiles was from 24 August to 28 October 2014, because it holds the pre, during and post-election where there was greater candidate drive level and his team marketing in social media selected for analysis. The results indicate that mobilization strategy (online and offline), merged with the promotion schedule, it is predominant in the social media Cassio. They also indicate that they do not show the failure of the campaign of the candidate in 2014.

Aproximaciones a los usos y apropiación de Twitter en bibliotecas universitarias de Argentina

El artículo analiza los principales usos de la plataforma Twitter por parte de bibliotecas universitarias en Argentina. Luego de la revisión bibliográfica, se exponen los procedimientos metodológicos empleados para identificar las instituciones que cuentan con esta herramienta comunicacional en la actualidad y los usos que se hace de ella a partir de ítems tales como: datos institucionales básicos, visibilidad y accesibilidad de la cuenta de Twitter en la Web de la biblioteca, momento de inicio de la actividad, volumen histórico de tuits, seguidores y siguiendo, así como cantidad y tipo de publicaciones realizadas en el período del relevamiento de datos. En los resultados se observa que un escaso número de bibliotecas adoptaron Twitter y que la apropiación de la herramienta muestra en general usos no planificados más centrados en la difusión que la interacción con usuarios. Se proponen estudios complementarios para conocer rutinas laborales de los bibliotecarios al respecto

Aproximaciones a los usos y apropiación de Twitter en bibliotecas universitarias de Argentina

El artículo analiza los principales usos de la plataforma Twitter por parte de bibliotecas universitarias en Argentina. Luego de la revisión bibliográfica, se exponen los procedimientos metodológicos empleados para identificar las instituciones que cuentan con esta herramienta comunicacional en la actualidad y los usos que se hace de ella a partir de ítems tales como: datos institucionales básicos, visibilidad y accesibilidad de la cuenta de Twitter en la Web de la biblioteca, momento de inicio de la actividad, volumen histórico de tuits, seguidores y siguiendo, así como cantidad y tipo de publicaciones realizadas en el período del relevamiento de datos. En los resultados se observa que un escaso número de bibliotecas adoptaron Twitter y que la apropiación de la herramienta muestra en general usos no planificados más centrados en la difusión que la interacción con usuarios. Se proponen estudios complementarios para conocer rutinas laborales de los bibliotecarios al respecto

Aproximaciones a los usos y apropiación de Twitter en bibliotecas universitarias de Argentina

El artículo analiza los principales usos de la plataforma Twitter por parte de bibliotecas universitarias en Argentina. Luego de la revisión bibliográfica, se exponen los procedimientos metodológicos empleados para identificar las instituciones que cuentan con esta herramienta comunicacional en la actualidad y los usos que se hace de ella a partir de ítems tales como: datos institucionales básicos, visibilidad y accesibilidad de la cuenta de Twitter en la Web de la biblioteca, momento de inicio de la actividad, volumen histórico de tuits, seguidores y siguiendo, así como cantidad y tipo de publicaciones realizadas en el período del relevamiento de datos. En los resultados se observa que un escaso número de bibliotecas adoptaron Twitter y que la apropiación de la herramienta muestra en general usos no planificados más centrados en la difusión que la interacción con usuarios. Se proponen estudios complementarios para conocer rutinas laborales de los bibliotecarios al respecto

El impacto de la presencia online en las cuentas de Twitter de las universidades vascas y navarras

Las Universidades han tenido que adaptarse a los nuevos modelos de comunicación surgidos en la época de Internet. Dentro de estos nuevos paradigmas las redes sociales han irrumpido y Twitter se ha establecido como una de las más importantes. El objetivo de esta investigación es demostrar que existe una relación entre la presencia online de una Universidad, definida por la cantidad de información disponible en Internet, y su cuenta en Twitter. Para ello se analizó la relación entre la presencia online y los perfiles oficiales de las cinco universidades del País Vasco y Navarra. Los resultados demostraron la existencia de una correlación significativa entre la presencia online de las instituciones y el número de seguidores de sus respectivas cuentas. En segundo lugar, esta investigación se planteó si Twitter puede servir para potenciar la presencia online de una Universidad. Es por eso que se formuló una segunda hipótesis que buscaba analizar si tener varias cuentas en Twitter aumentaría la presencia online de las Universidades. Los hallazgos para esta segunda hipótesis demostraron una correlación muy significativa entre tener varios perfiles en Twitter y la presencia online de las Universidades. Así queda demostrada la importancia de la presencia online para las cuentas de Twitter y la relevancia de Twitter a la hora de potenciar la presencia online de los centros.

Programas de tertulia política en Twitter: un modelo neurocomunicativo de análisis del discurso

En este trabajo aplicamos a la red social Twitter un modelo de análisis del discurso político y mediático desarrollado en publicaciones previas, que permite hacer compatible el estudio de los datos discursivos con propuestas explicativas surgidas a propósito de la comunicación política (neurocomunicación) y de la comunicación digital (la red como quinto estado, convergencia, inteligencia colectiva). Asumimos que hay categorías del encuadre discursivo (frame) que pueden ser tratadas como indicadores de habilidades cognitivas y comunicativas. Analizamos estas categorías agrupándolas en tres dimensiones fundamentales: la intencional (ilocutividad del tuit, encuadre interpretativo de las etiquetas), referencial (temas, protagonistas), e interactiva (alineamiento estructural, predictibilidad; marcas de intertextualidad y dialogismo; afiliación partidista). El corpus consta de 4116 tuits: 3000 tuits pertenecientes a los programas Al Rojo Vivo (La Sexta: A3 Media), Las Mañanas Cuatro (Cuatro: Mediaset) y Los Desayunos de TVE (RTVE), 1116 tuits de seguidores de los programas, que corresponden a 45 tuits de cada programa. Los resultados confirman que el modelo permite establecer diferentes perfiles de subjetividad política en las cuentas de Twitter.