An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration

@article{Mehmood2020AnEL,
  title={An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration},
  author={Yasir Mehmood and Vimala Balakrishnan},
  journal={Online Inf. Rev.},
  year={2020},
  volume={44},
  pages={1097-1117}
}
Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.,An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from… Expand
1 Citations
Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM
TLDR
The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks. Expand

References

SHOWING 1-10 OF 46 REFERENCES
Dictionary Based Approach to Sentiment Analysis-A Review
Due to the fast growth of World Wide Web the online communication has increased. In recent times th communication focus has shifted to social networkin g. I order to enhance the text methods ofExpand
Sentiment Analysis of Social Issues
Sentiment analysis refers to a broad range of fields of natural language processing, computational linguistics, and text mining. Sentiment classification of reviews and comments has emerged as theExpand
Detecting sentiment embedded in Arabic social media - A lexicon-based approach
TLDR
A novel framework for sentiment detection in Arabic tweets is introduced by translating the SentiStrength English sentiment lexicon into Arabic and afterwards the lexicon was expanded using Arabic thesauri. Expand
SentiVerb system: classification of social media text using sentiment analysis
TLDR
Novel frameworks of SentiVerb and Spell Checker system, which extracts the reaction, mood, and opinion of users from social media text (SMT) are proposed, and a new concept of threshold negative parameter is first time introduced in this article. Expand
Improving sentiment scoring mechanism: a case study on airline services
TLDR
Investigation of the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia finds that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL). Expand
Detecting tension in online communities with computational Twitter analysis
TLDR
Results indicate that a combination of conversation analysis methods and text mining outperforms a number of machine learning approaches and a sentiment analysis tool at classifying tension levels in individual tweets. Expand
A sentiment analysis of U.S. local government tweets: The connection between tone and citizen involvement
TLDR
It is argued that positive tone is only one part of a successful social media interaction plan, and social media managers to actively manage platforms to use activities that spur participation. Expand
Contextual semantics for sentiment analysis of Twitter
TLDR
Different from typical lexicon-based approaches, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Expand
Like It or Not
TLDR
Fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention are discussed. Expand
Unsupervised sentiment analysis with emotional signals
TLDR
This work investigates whether the signals in social media can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation and incorporates the signals into an unsupervised learning framework for sentiment analysis. Expand
...
1
2
3
4
5
...