Combining a large sentiment lexicon and machine learning for subjectivity classification

Lu, Bin; Tsou, Benjamin K.

doi:10.1109/icmlc.2010.5580672

Cited by 24 publications

(12 citation statements)

References 9 publications

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“…Machine learning approach produced high accuracy due to its high-quality training data. However, the performance drops when the same classifier is implemented in a different domain [27]. In contrast, lexicon based approach can be implemented in various domains, but slightly less accurate to machine learning approach.…”

Section: B Lexicon-based Approachmentioning

confidence: 99%

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Zabha¹,

Ayop²,

Anawar³

et al. 2019

ijacsa

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Sentiment analysis is a process of detecting and classifying sentiments into positive, negative or neutral. Most sentiment analysis research focus on English lexicon vocabularies. However, Malay is still under-resourced. Research of sentiment analysis in Malaysia social media is challenging due to mixed language usage of English and Malay. The objective of this study was to develop a cross-lingual sentiment analysis using lexicon based approach. Two lexicons of languages are combined in the system, then, the Twitter data were collected and the results were determined using graph. The results showed that the classifier was able to determine the sentiments. This study is significant for companies and governments to understand people's opinion on social network especially in Malay speaking regions.

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Section: B Lexicon-based Approachmentioning

confidence: 99%

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Zabha¹,

Ayop²,

Anawar³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…ML algorithms are commonly used for their simplicity and domain adaptability since it has the ability to learn from the training data. On the other hand, Lexicon-based algorithms are also frequently used to tackle general SA issues because they are scalable and computationally efficient [20,39,58]. Moreover, the choice of the subjectivity detection algorithm could effectively improve the overall performance of sentiment classification.…”

Section: Discussionmentioning

confidence: 99%

A survey on sentiment analysis in tourism

Anis

Saad

Aref

2020

IJICIS

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“…Obtaining large size of training data annotated by experts is difficult, and sometimes human judgment of the sentiment expressed in text is not as accurate as an automated approach. To overcome these difficulties, recently there were reports that combined both techniques (Lu &Tsou, 2010;Tan et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Twitter Sentiment toward Midterm Exams

Hu¹

2012

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Twitter is the most popular microblogging service today, with millions of its uers posting short messages (tweets) everyday. This huge amount of user-generated content contains rich factual and subjective information ideal for computational analysis. Current research findings suggest that Twitter data could be utilized to gain accurate public sentiment on various topics and events. With help of Twitter Stream API, we collected 260,749 tweets on the subject of midterm exams from students on Twitter for two consecutive weeks (Oct 17-Oct 30, 2011). Our aim was to investigate the real-time Twitter sentiment on midterm exams by hour, day, and week for these two weeks, using a sentiment predictor built from an opinion lexicon augmented for this specific domain. At different levels of temporal granularity, our analysis revealed the variation of sentiment. The average sentiment of the first week (Oct 17-23) was more negative than the second week (Oct 24-30). For both weeks, the overall trend curves of sentiment increased from Monday to Sunday. For each weekday, there was a period around 9:00 am-5:00 pm EST that had maximum sentimet. On each weekend, the sentiment values during a day reached their maximum between 5:00 am to 8:00 am, and then decreased after 8:00am. Furthermore, we observed some consistent group behavior of Twitter users based on seemingly random behavior of each individual. The lowest number of tweets always occured around 5:00 am-6:00 am each day, and the maximum number was around 1:00 pm except Sunday. The minimum of tweet lengths happened usually around 9:00 am and the maximum length was around 4:00 am everyday. Twitter users with positive sentiment appeared to have more friends and followers than those carrying negative sentiment. Also, users who shared the same sentiment inclined to have similar ratios of friends and followers, which is not true for general users

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Combining a large sentiment lexicon and machine learning for subjectivity classification

Cited by 24 publications

References 9 publications

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

Developing Cross-lingual Sentiment Analysis of Malay Twitter Data Using Lexicon-based Approach

A survey on sentiment analysis in tourism

Real-Time Twitter Sentiment toward Midterm Exams

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