Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions

2020 ◽  
Vol 12 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Al Hafiz Akbar Maulana Siagian ◽  
Masayoshi Aritsugi
Keyword(s):  
N Gram

Learning N-Gram Language Models from Uncertain Data

2016 ◽  
Author(s):  
Vitaly Kuznetsov ◽  
Hank Liao ◽  
Mehryar Mohri ◽  
Michael Riley ◽  
Brian Roark
Keyword(s):  
Uncertain Data ◽  
Language Models ◽  
N Gram

Rescore in a Flash: Compact, Cache Efficient Hashing Data Structures for n-Gram Language Models

2020 ◽  
Author(s):  
Grant P. Strimel ◽  
Ariya Rastrow ◽  
Gautam Tiwari ◽  
Adrien Piérard ◽  
Jon Webb
Keyword(s):  
Data Structures ◽  
Language Models ◽  
Cache Efficient ◽  
N Gram

scholarly journals OCR correction for Indonesian historic newspapers using word repetition, stemmer and n-gram

2019 ◽  
Vol 1193 ◽  
pp. 012032
Author(s):  
D Purwantoro ◽  
H Akbar ◽  
A Hidayati ◽  
Sfenrianto
Keyword(s):  
Word Repetition ◽  
N Gram

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

scholarly journals Stemmer and phonotactic rules to improve n-gram tagger-based indonesian phonemicization

2021 ◽  
Author(s):  
Suyanto Suyanto ◽  
Andi Sunyoto ◽  
Rezza Nafi Ismail ◽  
Ema Rachmawati ◽  
Warih Maharani
Keyword(s):  
N Gram

scholarly journals n-Gram Based Language Processing using Twitter Dataset to Identify COVID-19 Patients

2021 ◽  
pp. 103048
Author(s):  
Nidal Nasser ◽  
Lutful Karim ◽  
Ahmed El Ouadrhiri ◽  
Asmaa Ali ◽  
Nargis Khan
Keyword(s):  
Language Processing ◽  
N Gram

scholarly journals METING: A Robust Log Parser Based on Frequent n-Gram Mining

2020 ◽  
Author(s):  
Oihana Coustie ◽  
Josiane Mothe ◽  
Olivier Teste ◽  
Xavier Baril
Keyword(s):  
N Gram

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

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