Express your Emotions

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Female , still study , age 26 , love adventures

3 years ago

My Topic totally depends on sentiment analysis in every field of life sentiment play a vital role and in the data science field, it is the hottest area, and over 7000 research paper published on it. Sentiment analysis is a computational process to identify positive or negative sentiments expressed in a piece of text. In this paper, we present a sentiment analysis system for Roman Urdu. It is becoming increasingly used because people prefer to communicate on the web using Latin Script (26 English Alphabets), instead of typing in their language using their language-speciﬁc keyboards. The objective of this work is to develop a baseline sentiment analysis system for Roman Urdu. Sentence level sentiment analysis classiﬁes sentences as per their polarity. Aspect or attribute level sentiment analysis classiﬁes the sentiment of speciﬁc attributes of a target entity. Sentiment analysis is not simple for all cases. Treebank techniques are good approaches to handle sentiment analysis with the handling of semantic-based scenarios. For this, a Recursive Neural Tensor Network used for sentiment analysis produced accuracies ranging from 80–85 percent There are three broad approaches to sentiment analysis. First is the machine learning approach, which predicts the polarity of the sentiment using training, testing, and optional development data. For this, the training data needs to be hand-labeled. Second is the lexicon-based approach which exploits a list based methodology of predeﬁned words to determine the polarity of sentiment. The third is a hybrid approach that combines machine learning and lexicon-based approaches. Sentiment analysis classiﬁcation is done at three levels

My Topic totally depends on sentiment analysis in every field of life sentiment play a vital role and in the data science field, it is the hottest area, and over 7000 research paper published on it. Sentiment analysis is a computational process to identify positive or negative sentiments expressed in a piece of text. In this paper, we present a sentiment analysis system for Roman Urdu. It is becoming increasingly used because people prefer to communicate on the web using Latin Script (26 English Alphabets), instead of typing in their language using their language-speciﬁc keyboards. The objective of this work is to develop a baseline sentiment analysis system for Roman Urdu. Sentence level sentiment analysis classiﬁes sentences as per their polarity. Aspect or attribute level sentiment analysis classiﬁes the sentiment of speciﬁc attributes of a target entity. Sentiment analysis is not simple for all cases. Treebank techniques are good approaches to handle sentiment analysis with the handling of semantic-based scenarios. For this, a Recursive Neural Tensor Network used for sentiment analysis produced accuracies ranging from 80–85 percent There are three broad approaches to sentiment analysis. First is the machine learning approach, which predicts the polarity of the sentiment using training, testing, and optional development data. For this, the training data needs to be hand-labeled. Second is the lexicon-based approach which exploits a list based methodology of predeﬁned words to determine the polarity of sentiment. The third is a hybrid approach that combines machine learning and lexicon-based approaches. Sentiment analysis classiﬁcation is done at three levels

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Written by

Female , still study , age 26 , love adventures

3 years ago

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