Aspect-Based Sentiment Analysis Using Aspect Map

Abstract

Aspect-based sentiment analysis (ABSA) is the task of classifying the sentiment of a specific aspect in a text. Because a single text usually has multiple aspects which are expressed independently, ABSA is a crucial task for in-depth opinion mining. A key point of solving ABSA is to align sentiment expressions with their proper target aspect in a text. Thus, many recent neural models have applied attention mechanisms to learning the alignment. However, it is problematic to depend solely on attention mechanisms to achieve this, because most sentiment expressions such as "nice" and "bad" are too general to be aligned with a proper aspect even through an attention mechanism. To solve this problem, this paper proposes a novel convolutional neural network (CNN)-based aspect-level sentiment classification model, which consists of two CNNs. Because sentiment expressions relevant to an aspect usually appear near the aspect expressions of the aspect, the proposed model first finds the aspect expressions for a given aspect and then focuses on the sentiment expressions around the aspect expressions to determine the final sentiment of an aspect. Thus, the first CNN extracts the positional information of aspect expressions for a target aspect and expresses the information as an aspect map. Even if there exist no data with annotations on direct relation between aspects and their expressions, the aspect map can be obtained effectively by learning it in a weakly supervised manner. Then, the second CNN classifies the sentiment of the target aspect in a text using the aspect map. The proposed model is evaluated on SemEval 2016 Task 5 dataset and is compared with several baseline models. According to the experimental results, the proposed model does not only outperform the baseline models but also shows state-of-the-art performance for the dataset.

Targeted Aspect-Based Sentiment Analysis for Lithuanian Social Media Reviews

“…Knowledge-base for entity recognition and linking was constructed on official data, including fields of studies, study programs and universities and their colloquialisms in social media. NER rule-based matcher was implemented using spaCy 7…”

“…Different architectures are considered, especially neural sequential models, such as long short-term memory (LSTM) networks with different modifications -hierarchical attention mechanism [4,5], delayed memory update [6]. Another approach suggests convolutional neural network based (CNN-based) aspect-level sentiment classification models, consisting of two CNNs [7].…”

Self-Attention Networks and Adaptive Support Vector Machine for aspect-level sentiment classification

“…However, all these methods lack of semantic logicality and fluency for context divided by aspect-specific, hence the representation of context is continuous so that the semantic information is not fluent. Yunseok Noh (Noh et al 2019) applied CNN as Aspect-Map Extraction Network (AMEN) to extract the representation of aspect to constitute an aspect map, then regarded the representations of aspect and context as input to conduct sentiment classification. Attention over Attention mechanism was introduced to model the interactive relationships between aspect-specific and every word of text, which can assign the greater weights to some important words on sentiment classification (Huang et al 2018).…”

LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model

“…Udit et al [14] developed an improved sentiment analysis method using image processing techniques based on visual data. Other research related to aspect-based sentiment analysis (ABSA) classify the sentiment of a specific aspect in a text presented in [15][16][17]. Earlier research has shown that these approaches are appropriate only under certain conditions.…”