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Register a new userInteractive Learning Approach for Arabic Target-Based Sentiment Analysis
نهج التعلم التفاعلي لتحليل المعنويات العربية المستندة إلى الهدف
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في الآونة الأخيرة، يركز غالبية الباحثين تحليل المعنويات على تحليل المعنويات المستندة إلى الهدف لأنه يوفر تحليلا متعمقا بنتائج أكثر دقة بالمقارنة مع تحليل المعنويات التقليدية.في هذه الورقة، نقترح نهجا تعليميا تفاعليا لمعالجة مهمة تحليل المعنويات المستندة إلى الهدف للغة العربية.يستخدم نموذج IA-LSTM المقترح آلية تفاعلية تعتمد على الانتباه لإجبار النموذج على التركيز على أجزاء مختلفة (أهداف) من الجملة.نحن نبحث في القدرة على استخدام الأهداف والحق الأيمن والأيسر، ونموذجها بشكل منفصل لتعلم تمثيلاتهم الخاصة عبر النمذجة التفاعلية.قمنا بتقييم نموذجنا على مجموعة بيانات مختلفة: مراجعة الفنادق العربية ومجموعات بيانات مراجعة الكتاب العربية.توضح النتائج فعالية استخدام هذه التقنية النمذجة التفاعلية للمهمة القائمة على الأهداف العربية.حصلت النموذج على قيم دقة 83.10 مقارنة بنماذج Sota مثل AB-LSTM-PC والتي حصلت على 82.60 لنفس مجموعة البيانات.
Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IA-LSTM model uses an interactive attention-based mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, right, and left context, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: Arabic hotel review and Arabic book review datasets. The results demonstrate the effectiveness of using this interactive modeling technique for the Arabic target-based task. The model obtained accuracy values of 83.10 compared to SOTA models such as AB-LSTM-PC which obtained 82.60 for the same dataset.
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Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding
of the contextual information, especially the particular patterns that suggest the sentiment polarity. However, these patterns typically vary in different sentences, especially when the sentences come from different sources (domains), which makes ABSA still very challenging. Although combining labeled data across different sources (domains) is a promising solution to address the challenge, in practical applications, these labeled data are usually stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. Particularly, TM aims to identify different isolated data sources due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.
Both the issues of data deficiencies and semantic consistency are important for data augmentation. Most of previous methods address the first issue, but ignore the second one. In the cases of aspect-based sentiment analysis, violation of the above is
sues may change the aspect and sentiment polarity. In this paper, we propose a semantics-preservation data augmentation approach by considering the importance of each word in a textual sequence according to the related aspects and sentiments. We then substitute the unimportant tokens with two replacement strategies without altering the aspect-level polarity. Our approach is evaluated on several publicly available sentiment analysis datasets and the real-world stock price/risk movement prediction scenarios. Experimental results show that our methodology achieves better performances in all datasets.
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Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum,
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