aspect-based sentiment analysis using convolutional neural networks and class activation mapping

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content: introduction…………………………………………………………….2 chapter 1. related work 1.1. aspect-based sentiment analysis…………………………………………..7 1.2. aspect-based sentiment analysis techniques and approaches………...10 1.3. application of aspect extraction in recommender systems……………..13 chapter 2. aspect selection 2.1. supervised and unsupervised……………………………………………..15 2.2.procedures and practices in aspect-based sentiment analysi…………….18 2.3.related works and proposed method……………………………………….21 conclusion………………………………………………………………..24 glossary…………………………………………………………………...23 bibliography……………………………………………………………..25 introduction 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 …

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essions, 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. as our honorable president shavkat mirziyoyev[footnoteref:1] said that: [1: “xalq so‘zi”, 2022 yil 16 fevral.] at a video conference chaired by president shavkat mirziyoyev, the issues of analysis of aspect development were discussed in detail. it was stated that the ministry of public aspect should not be engaged in the construction of an analysis of aspect, but in improving the quality of education, strengthening the knowledge of teachers. in general, a single text contains multiple aspects and the aspects are expressed independently. thus, for in-depth sentiment analysis, the sentiments of a text should be examined at aspect level, not at text level. several methods have been proposed for aspect-based sentiment analysis (absa) . because a target …

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been used to generate a context vector that selectively embodies informative expressions for aspect-level sentiment classification. the main problem of the context vector is that it loses positional information of salient words, even if the information is important in absa to know where the aspect expressions of a target aspect appear. for instance, figure 1 shows an example review on a restaurant with two different target aspects and their corresponding sentiment expressions. in this figure, one target aspect is location represented with an aspect expression “view of river”, and the other is food expressed as “sushi rolls”. note that the positive opinion about location can be found by looking at the leftmost word “nice”. similarly, the negative word “bad” for the aspect food is found near the aspect expression “sushi rolls”. that is, the sentiment expressions appear near their aspect expressions. if a corpus in which aspect expressions and their …

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g to the aspect map. note that a corpus where the aspects are annotated at word level is needed to learn aspect map extraction. however, such a corpus is not available as stated above. therefore, the aspect map should be extracted in a weakly supervised manner. recently, the class activation map (cam) has been proposed for weakly supervised object localization in the community of computer vision [24]. it localizes the objects of an image by linking the feature maps of cnn to relevant classes where the linking is implemented by global average pooling. thus, the network supported by cam can learn the localization of objects from the weakly labeled images, and is similar to the aspect map extraction from weakly labeled texts. the cnns for text analysis usually have a single convolutional layer with various kernel sizes unlike those for image analysis. thus, they can capture n-gram word patterns for …

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elu gates are used to amplify sentiment features from tanh gates, thus the gated cnn tries to solve the problem of the context vector of attention mechanisms. unlike this work which just utilizes aspect embeddings to generate aspect features and learns all features under the sentiment classification objective, the proposed model learns the relationship between words and aspects separately from the sentiment classification. consequently, the proposed model can obtain a certain aspect map and can effectively exploit the aspect map for absa. the rest of this paper is organized as follows. in section 2, we briefly introduce previous work on recent neural models for aspect-based sentiment analysis. in section 3, we describe the overall process of the proposed model, and then, in section 4, we explain the detailed architectures and mechanisms of two cnns used in the proposed model. the experimental results are given in section 5. finally, we conclude …

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content: introduction…………………………………………………………….2 chapter 1. related work 1.1. aspect-based sentiment analysis…………………………………………..7 1.2. aspect-based sentiment analysis techniques and approaches………...10 1.3. application of aspect extraction in recommender systems……………..13 chapter 2. aspect selection 2.1. supervised and unsupervised……………………………………………..15 2.2.procedures and practices in aspect-based sentiment analysi…………….18 2.3.related works and proposed method……………………………………….21 conclusion………………………………………………………………..24 glossary…………………………………………………………………...23 bibliography……………………………………………………………..25 introduction 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 independe...

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