Social Data Sentiment Analysis in Smart Environments - Extending Dual Polarities for Crowd Pulse Capturing

@inproceedings{Vakali2013SocialDS,
  title={Social Data Sentiment Analysis in Smart Environments - Extending Dual Polarities for Crowd Pulse Capturing},
  author={Athena Vakali and Despoina Chatzakou and Vassiliki A. Koutsonikola and George Andreadis},
  booktitle={DATA},
  year={2013}
}
Social networks drive todays opinion and content diffusion. Humans interact in social media on the basis of their emotional states and it is important to capture people emotional scales for a particular theme. Such interactions are facilitated and become evident in smart environments characterized by mobile devices and new smart city contexts. This work proposes a sentiment analysis approach which extends positive and negative polarity in higher and wider emotional scales to offer new smart… 

Figures from this paper

Visual sentiment prediction with transfer learning and big data analytics for smart cities

The objective of this article is to help fill the void by reviewing the state of the art and opportunities of data sources and applications of sentiment analysis for smart cities and explores deep features of photos shared by users in Twitter via transfer learning.

Social media sentiment monitoring in smart cities: an application to Moroccan dialects

A set of features that have been used with machine learning techniques, sentiment analysis, text classification, and text classification to extract the intelligence needed from social media feeds containing Moroccan dialects are presented.

Smart Citizen Sensing: A Proposed Computational System with Visual Sentiment Analysis and Big Data Architecture

This work explores deep features of photos shared by users in Twitter via convolutional neural networks and transfer learning to predict sentiments, and proposes big data architecture to extract, save and transform raw Twitter image posts into useful insights.

Machine Learning based Sentiment Analysis using Graph Based Approach

  • Monali BordoloiS. K. Biswas
  • Computer Science, Economics
    2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
  • 2019
An efficient sentiment analysis model is proposed by using a popular graph based method and some standard machine learning techniques in order to produce an overall sentiment using a limited and important set of keywords.

THE AWARENESS OF ENVIRONMENT CONSERVATION BASED ON OPINION DATA MINING FROM SOCIAL MEDIA

This study shows that social media effects to build and raise the awareness of environment protection and furthermore this research can apply to other fields and industries aspects as well.

An Environment for Collective Perception based on Fuzzy and Semantic Approaches

This environment relies upon semantic knowledge discovery techniques and fuzzy computational approaches, including natural language processing, sentiment analysis, POI signatures and Fuzzy Cognitive Maps, to effectively gather the realistic perception of a user community towards given areas and attractions of a Smart City.

Smart Cities Data Streams Integration: experimenting with Internet of Things and social data flows

The SmartSantander infrastructure (EU FP7 project) has offered the ground for the SEN2SOC experiment which has integrated sensor and social data streams and its research and industrial perspective and potential impact are outlined.

Social networks in smart cities: Comparing evaluation models

This paper uses literature findings in order to demonstrate how SN can be evaluated in respect to the smart city and alternative model choices are discovered and follows a multi-criteria decision making process with the contribution of smart city experts.

References

SHOWING 1-10 OF 14 REFERENCES

Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena

It is speculated that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.

Emotional Aware Clustering on Micro-blogging Sources

An emotional aware clustering approach which performs sentiment analysis of users tweets on the basis of an emotional dictionary and groups tweets according to the degree they express a specific set of emotions is proposed.

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

This paper shows how to automatically collect a corpus for sentiment analysis and opinion mining purposes and builds a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

This work connects measures of public opinion measured from polls with sentiment measured from text, and finds that temporal smoothing is a critically important issue to support a suc- cessful model.

Indentifying Emotional Characteristics from Short Blog Texts

Using 50 and 200 word samples of naturally-occurring blog texts, automated content analysis shows that some emotions are more discernible than others, and relates this finding to human emotion perception and note potential applications.

Compositional Matrix-Space Models for Sentiment Analysis

This paper presents the first such algorithm for learning a matrix-space model for semantic composition, and its experimental results show statistically significant improvements in performance over a bag-of-words model.

Lexicon-Based Methods for Sentiment Analysis

The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation, and is applied to the polarity classification task.

Thumbs up? Sentiment Classification using Machine Learning Techniques

This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.

Sentiment Analysis: Adjectives and Adverbs are Better than Adjectives Alone

This work proposes an AAC-based sentiment analysis technique that uses a linguistic analysis of adverbs of degree that leads to higher accuracy based on Pearson correlation with human subjects and describes the results of experiments.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.