Tokenizing micro-blogging messages using a text classification approach

Laboreiro, Gustavo; Sarmento, Luís; Teixeira, Jorge; Oliveira, Eugénio

doi:10.1145/1871840.1871853

Cited by 27 publications

(7 citation statements)

References 10 publications

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“…Bermingham and Smeaton [2] suggest that it is easier to infer the sentiment polarity of micro-blogging posts compared with blogs, which have richer textual content (they also note the similarity between micro-blogging posts and SMS messages). However, the automatic processing of micro-blogging posts can be problematic because of the use of non-standard words and unusual punctuation [9]. Leong, et al [10] use sentiment mining to analyze SMS messages in teaching evaluations.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Message Sentiment Analysis

Andriotis

Takasu

Tryfonas

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Humans tend to use specific words to express their emotional states in written and oral communications. Scientists in the area of text mining and natural language processing have studied sentiment fingerprints residing in text to extract the emotional polarity of customers for a product or to evaluate the popularity of politicians. Recent research focused on micro-blogging has found notable similarities between Twitter feeds and SMS (short message service) text messages. This paper investigates the common characteristics of both formats for sentiment analysis purposes and verifies the correctness of the similarity assumption. A lexicon-based approach is used to extract and compute the sentiment scores of SMS messages found on smartphones. The data is presented along a timeline that depicts a sender's emotional fingerprint. This form of analysis and visualization can enrich a forensic investigation by conveying potential psychological patterns from text messages.

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Section: Introductionmentioning

confidence: 99%

Smartphone Message Sentiment Analysis

Andriotis

Takasu

Tryfonas

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…Tokenization is important step in natural language processing and potentially affects the sentiment analysis of the texts used in the social media (Laboreiro et al, 2010;Bird et al, 2009). Morphologically, Arabic language is very rich , and hence the Arabic tweets text were cleaned by deleting the non-Arabic words characters and special Twitter characters from the sentence.…”

Section: Tokenization Cleaning and Normalizationmentioning

confidence: 99%

Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting

Altaher

2017

Int. j. adv. appl. sci.

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The increasing adoption of social media networks as a platform for sharing opinions on different aspects emerged the sentiment analysis and opinion mining as an active research area. Recently, the sentiment analysis on Twitter has attracted considerable attention due to its many applications in various aspects of our lives. Many approaches have been presented for sentiment analysis based on English language, thus there is a need for efficient sentiment analysis approaches for Arabic language, since it has different structure when compared to other languages. This paper proposes a hybrid approach for sentiment analysis of Arabic tweets based on two stages. Firstly, the pre-processing methods like stop-word removal, tokenization and stemming are applied, and then two features weighting algorithms (information gain and chai square) are utilized to assign high weights to the most significant features of the Arabic tweets. Secondly, the deep learning technique is employed to effectively and accurately classify the Arabic tweets either as positive or negative tweets. The performance of the proposed approach has been compared with some of the classification methods such as Decision Tree (DT), Neural Networks (NN) and Support Vector Machine (SVM) using the dataset collected from Arabic tweets. The proposed approach outperforms the other approaches and achieved highest accuracy and precision of 90% and 93.7%, respectively.

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“…As pointed out by Laboreiro, Sarmento, Teixeira, and Oliveira (2010), tokenization significantly affects sentiment analysis, especially in the case of social media. Although Ark-tweet-nlp tool (Gimpel et al, 2011) was developed and tested in English, it yields satisfactory results in Czech as well, according to our initial experiments on the Facebook corpus.…”

Section: Preprocessingmentioning

confidence: 99%