{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:21Z","timestamp":1740202041975,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>The proliferation of microblogging services present a new collection of obstacles and opportunities to automatically extract and analyze the content to understand the expressed sentiments. Several approaches are introduced ranging from traditional information extraction techniques, incorporation of machine learning and utilization of lexicons. However, existing works mainly introduced lexicon as the pair of certain dataset which resulted to limited analytical coverage when used in unseen dataset. Therefore, this research aims to investigate the performance of a composite lexicon in terms of accuracy. A new method called Adverb Effect on Adjective (AEA) is also introduced to calculate the intensity of the sentiment. An application called SentiMiner is developed based on these methods and implemented to analyze Facebook posts. It is observed that the combination of the AEA method with the composite lexicon has achieved the highest accuracy when compared against benchmarking algorithms when tested on the Amazon product review dataset. The theme extraction experiment has also shown that the SentiMiner's approach has defeated the AlchemyAPI algorithm when used on the Stanford Sentiment dataset. Therefore, it is concluded that wide coverage of lexicon and sentiment polarity strength as the content-based analysis method can significantly improve the accuracy of sentiment scoring in microblogging mining.<\/jats:p>","DOI":"10.3233\/978-1-61499-434-3-398","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:26:53Z","timestamp":1740133613000},"source":"Crossref","is-referenced-by-count":0,"title":["Content-Based Analysis Method for Sentiment Scoring in Microblogging Mining"],"prefix":"10.3233","author":[{"family":"Mohd Sharef Nurfadhlina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Haghanikhameneh Fartash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Software Methodologies, Tools and Techniques"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:13:02Z","timestamp":1740136382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=265&spage=398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-434-3-398","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}