458 research outputs found
Sentiment Lexicon Adaptation with Context and Semantics for the Social Web
Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics' feelings towards policies, brands, business, etc. General purpose sentiment lexicons have been used to compute sentiment from social streams, since they are simple and effective. They calculate the overall sentiment of texts by using a general collection of words, with predetermined sentiment orientation and strength. However, words' sentiment often vary with the contexts in which they appear, and new words might be encountered that are not covered by the lexicon, particularly in social media environments where content emerges and changes rapidly and constantly. In this paper, we propose a lexicon adaptation approach that uses contextual as well as semantic information extracted from DBPedia to update the words' weighted sentiment orientations and to add new words to the lexicon. We evaluate our approach on three different Twitter datasets, and show that enriching the lexicon with contextual and semantic information improves sentiment computation by 3.4% in average accuracy, and by 2.8% in average F1 measure
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
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Sentiment Analysis on Financial News and Microblogs
Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. This thesis highlights the importance of sentiment analysis, provides a summary of seminal works and different approaches towards sentiment analysis. It aims to address sentiment analysis on financial news and microblogs by classifying textual data from financial news and microblogs as positive or negative. Sentiment analysis is performed by making use of paragraph vectors and logistic regression in this thesis and it aims to compare it with previously performed approaches to performing analysis and help researchers in this field. This approach achieves state of the art results for the dataset used in this research. It also presents an insightful analysis of the results of this approach
Sentiment analysis on online social network
A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis
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
A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications
Sentiment analysis (SA) is an emerging field in text mining. It is the
process of computationally identifying and categorizing opinions expressed in a
piece of text over different social media platforms. Social media plays an
essential role in knowing the customer mindset towards a product, services, and
the latest market trends. Most organizations depend on the customer's response
and feedback to upgrade their offered products and services. SA or opinion
mining seems to be a promising research area for various domains. It plays a
vital role in analyzing big data generated daily in structured and unstructured
formats over the internet. This survey paper defines sentiment and its recent
research and development in different domains, including voice, images, videos,
and text. The challenges and opportunities of sentiment analysis are also
discussed in the paper.
\keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep
Learning, Natural Language Processing
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