Optimal Trained Bi-Long Short Term Memory for Aspect Based Sentiment Analysis with Weighted Aspect Extraction
Keywords:Aspect-based Sentiment Analysis, Stanford Dependency Passer, Association Rule Mining, Optimized Bi-LSTM, Optimization
Sentiment analysis based on aspects seeks to anticipate the polarities of sentiment in specified targets related to the text data. Several studies have shown a strong interest in using an attention network to represent the target as well as context on generating an efficient representation of features used for tasks while sentiment classification. Still, the attention score computation of the target using an average vector for context is unequal. While the interaction mechanism is simplistic, it needs to be overhauled. Therefore, this paper intends to introduce a novel aspect-based sentiment analysis with three phases: (i) Preprocessing, (ii) Aspect Sentiment Extraction, (iii) Classification. Initially, the input data is given to the preprocessing phase, in which the tokenization, lemmatization, and stop word removal are performed. From the preprocessed data, the weighted implicit and weighted explicit extraction is determined in the Aspect Sentiment Extraction. Moreover, the weighted implicit aspect extraction is done by Stanford Dependency Passer (SDP) method, and the weighted explicit extraction is done through proposed Association Rule Mining (ARM). Subsequently, the extracted features are provided to the classification phase in which the Optimized Bi-LSTM is utilized. For making the classification more accurate and precise, it is planned to tune the weights of Bi-LSTM optimally. For this purpose, an Opposition Learning Cat and Mouse-Based Optimization (OLCMBO) Algorithm will be introduced in this work. In the end, the outcomes of the presented approach are calculated to the extant approaches with respect to different measures like F1-measure, specificity, Negative Predictive Value (NPV), accuracy, False Negative Rate (FNR), sensitivity, precision, False Positive Rate(FPR), and Matthew’s correlation coefficient, respectively.
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