Buzzwords Build Momentum: Global Financial Twitter Sentiment and the Aggregate Stock Market

Groß-Klußmann, Axel; König, Stephan; Ebner, Martin

doi:10.2139/ssrn.3412908

Cited by 2 publications

(1 citation statement)

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“…Hiram Calvo, Arturo P. Rocha-Ramirez at.al(2019) Proposes a word senesce disambiguation model based on embedding representation of words using deep neural networks and obtained F1 Score 63.30.They used text processing tasks like convert text into lower case and applied Porter and Snowball stemming algorithms to remove suffixes [8].Axel Groß-Klußmann and at.al(2019) proposed Un-supervised and Supervised expert identification system to identify the major financial developments in economic regions and to predict profitable investments in stock market. They used Python NLTK to eliminate noise such as to removal of punctuations, stopwords, hashtags, casefolding, reduced the fraction of noise induced by informal language, applied Porter stemming algorithm on financial twitter datasets [10]. Vishal Vyas and V.Uma (2018) conducted experiments with Rapid Miner to analyze the tweets of sentiments and compared the accuracy levels with twenty different tools.…”

Section: Related Workmentioning

confidence: 99%

The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

Rao*,

2019

IJITEE

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Sentiment Analysis is an application of Natural Langue Processing to analyze social media corpora to extract insights of corpora. Sentiment analytical results are the real feedback of the customers, which enables the organizations and companies to take appropriate decision on their products and business policies. Stemming plays in-evitable and vital role in sentiment analysis. Stemming is one of the phase of preprocessing the social media corpora. Today most of the researches uses strong stemmers to identify stem words of social media corpora. The most popular stemming algorithms such as Lancaster and Porter stemming algorithms causes prejudiced the meaning of the words. The over-stemmed words mislead the sentiment classification process. To prevent the over-stemming the Unprejudiced lighter stemming algorithm is proposed to sustain the meaning of the stemmed words. The propose Un-prejudiced algorithm uses lexical database and Parts of speech of Python Natural Language Tool Kit. There are a few stemming algorithm accuracy evaluation methods, in this paper we focused on Paice Error-rate relative to truncation (ERRT) measure to evaluate the accuracy of Lancaster, Porter and Unprejudiced stemming algorithms. The experiments were conducted on 25,758 source words and results were evaluated using Paice stem evaluation method and Sirsat method. The Paice Evaluation ERRT values 0.47209, 0.28703, 0.15502 of Lancaster, Porter, Unprejudiced respectively are proved that the Unprejudiced stemmer is more accurate than Lancaster and Porter. Sirsat’s stem evaluation method Average Words Conflation Factor (AWCF) results 10310.31, 14031.17, 23349.87 of Lancaster, Porter, Unprejudiced respectively are also proved the Unprejudiced stemming algorithm is more accurate than Lancaster and Porter stemming algorithms.

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Section: Related Workmentioning

confidence: 99%