Semantic disambiguation in a social information discovery system

  title={Semantic disambiguation in a social information discovery system},
  author={Claudia Diamantini and Alex Mircoli and Domenico Potena and Emanuele Storti},
  journal={2015 International Conference on Collaboration Technologies and Systems (CTS)},
Sentiment Analysis of microblog content calls for specific tools able to cope with the dynamic nature of information published in social networks, and the intrinsic complexity and ambiguity of human language. In this work we introduce a Word Sense Disambiguation (WSD) algorithm for polysemous word disambiguation which uses a dictionary-based approach to determine the most fitting meaning of a term, basing on nearby words in the sentence. The work is a part of a Business Intelligence system for… 

Figures and Tables from this paper

A Negation Handling Technique for Sentiment Analysis
The problem of the automatic determination of the scope of negation is addressed and a negation handling algorithm based on dependency-based parse trees is presented, based on the use of grammatical relations among words to model a sentence, and hence to determine words that are affected by negation.
Emotion and sentiment analysis of tweets using BERT
This paper investigates the use of Bidirectional Encoder Representations from Transformers (BERT) models for both sentiment analysis and emotion recognition of Twitter data and evaluates the performance of the obtained models on real-world tweet datasets.
Automatic Emotional Text Annotation Using Facial Expression Analysis
The present work describes the main ideas of the proposal, presenting a four-phase methodology and discussing the main issues related to the selection of input frames and the processing of emotions resulting from facial expressions analysis.
Automatic Extraction of Affective Metadata from Videos Through Emotion Recognition Algorithms
This work presents a 3-phase methodology for the automatic extraction of emotional metadata from videos through facial expression recognition algorithms, and proposes a simple but versatile model for metadata that takes into account variations in emotions among video chunks.


Sentiment Analysis on Twitter
This paper proposes and investigates a paradigm to mine the sentiment from a popular real-time microblogging service, Twitter, where users post real time reactions to and opinions about “about” and “everything”.
Random Walk Weighting over SentiWordNet for Sentiment Polarity Detection on Twitter
A novel approach in Sentiment Polarity Detection on Twitter posts is presented, by extracting a vector of weighted nodes from the graph of WordNet, which is used on SentiWordNet to compute a final estimation of the polarity.
An integrated system for social information discovery
This work proposes a methodology to design and presents the experience gained in the development of an information discovery system based on Exploratory Data Analysis and aimed at analyzing text contents from two social networks: Facebook and Twitter.
Semantic Sentiment Analysis of Twitter
This paper introduces a novel approach of adding semantics as additional features into the training set for sentiment analysis by adding its semantic concept from tweets as an additional feature, and measures the correlation of the representative concept with negative/positive sentiment.
Twitter as a Corpus for Sentiment Analysis and Opinion Mining
A novel solution to target-oriented sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as “tweets” is introduced and it is shown that the hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarizing system.
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
Opinion Mining and Sentiment Analysis
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
WordNet: A Lexical Database for English
WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Mining opinions on the basis of their affectivity
  • D. PotenaC. Diamantini
  • Computer Science
    2010 International Symposium on Collaborative Technologies and Systems
  • 2010
An affective annotation model of shared reviews, which takes into account the affectivity expressed by the Web community is proposed, which can be exploited to better organize the collaborative work of reviews management, and to take advantage of reviews inside the community.
Sentiment Classification of Reviews Using SentiWordNet
This research presents the results of applying the SentiWordNet lexical resource to the problem of automatic sentiment classification of film reviews, and finds that results obtained are in line with similar approaches using manual lexicons seen in the literature.