Optimizing Machine Learning-based Sentiment Analysis Accuracy in Bilingual Sentences via Preprocessing Techniques

Mohammed Maree,Mujahed Eleyat,Enas Mesqali

#Machine learning #bilingual sentiment analysis #NLP #sentiment datasets

Mohammed Maree,Mujahed Eleyat,Enas Mesqali

#Machine learning #bilingual sentiment analysis #NLP #sentiment datasets

The International Arab Journal of Information Techn ology, Vol. 12, No. 3, May 2015

Semantic Boolean Arabic Information Retrieval

# AIR # semantic web # arabic language # ontology

#IR # stemming # prefix # infix # suffix # exception list s

Categorization in Arabic Text

#Root extraction # information retrieval # bigrams # stemming # Arabic morphological rules # feature selection

techniques. The enhancement includes applying new stemming technique “rule-based light stemming” to balance the negative

#Arabic speech transcription # topic clustering

#Sentiment analysis # subjective lexicon # punjabi language # n-gram modeling # support vector machine

balanced benchmark corpus. To accomplish this study ten Arabic lexicons were constructed manually, and a new tool called

#Big data # social networks # sentiment analysis # Arabic text classification # and analysis # opinion mining

The complexity of the Arabic morphology caused by multimode terms, using diacritics, letters have different forms according

#Arabic IR #light stemming #morphological analysis # affixes’ removal # term selection # Arabic document indexing

#Stemmer # natural language # light stemmers

Mubashir Ali1, Shehzad Khalid2, and Muhammad Saleemi2

#Urdu stemmer # infix classes # infix rules # stemming rules # stemming lists

Comments

#Opinion mining # sentiment analysis # K-Nearest Neighbours # Naïve Bayes # Support Vector Machines # Arabic # comment

Sait Yucebas,Rabia Tintin

#Natural language processing #stemming #morphological analysis #Turkish language