# Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification
## 1 Introduction  
With the rapid popularization of the Internet, the number of user-generated comments on social media applications has grown exponentially. In particular, social network services such as Twitter, provide a platform for specific users to easily share their personal preferences, opinions, and attitudes about various topics through short posts [1]. Understanding the abundant information of people's opinions expressed in these short posts can help companies to meet the emerging needs of downstream applications such as customer services [2], brand monitoring [3], and recommendation systems [4; 5]. For instance, in order to gain more revenue, businesses can adjust their marketing strategies according to the attitudes of specific users towards products and services. Therefore, sentiment classification [6] has been an active research direction in the field of natural language processing (NLP), which aims to automatically classify the sentiment polarity of a given text into negative, positive, or more fine-grained classes. An essential step of NLP is text representation learning, which is to encode the original text information into continuous vectors by constructing a projection from semantics to points in ahigh-dimensional space. The performance of text sentiment analysis completely depends on the comprehensive and high-quality representation of the raw text through representation learning models. Earlier efforts in modeling text for sentiment analysis are mainly based on statistical machine learning methods. The statistical machine learning-based representation models treat the task of text sentiment classification as the word frequency statistics problem, focusing on designing effective sentiment features to achieve better performance, such as n-grams [7], bag-of-words [8], and TF-IDF [9]. Specifically, the bag-of-words feature is to summarize the frequency of co-occurrence sentiment-related words to express the original text as the one-hot vector representation for sentiment classification, where the sentiment polarity of text is largely predicted to be negative if the number of negative sentiment-related words is more than that of the positive ones. Similar to bag-of-words, the n-grams feature shifts focus from co-occurrence sentiment-related words to sentiment-related word pair. The TF-IDF feature returns the weight scores of each word according to certain sentiment lexicon in different documents, which can help the computational process to be more easily implemented in sentiment classification task. These traditional sentiment features show the fact that sentiment linguistic knowledge is essentially a valuable resource that deserves further exploited in the process of text sentiment analysis. However, in real-world applications, sentiment linguistic knowledge is hardly useful without the information of context features. In general, the ambiguity and complexity of NLP make a great challenge for the simple frequency characteristics of words. Firstly, the concept of semantic composition does not exist in word frequency statistics approaches. For example, it will raise the problem about reverse polarity of sentiment when handling content word negation (e.g., 'ameliorates fear'), flipping negation (e.g., 'not sad') or unbounded dependencies (e.g., 'no one passed the selection'). Secondly, word frequency statistics methods cannot explicitly identify the single word sense, thus causing the word-level semantic confusion, e.g., unable to distinguish the meaning of word 'hot' in the phrases 'a hot temper' and 'a hot, kind girl'. Lastly, simple summation over sentiment-related words can also cause the sentence-level semantic confusion because it does not have the ability to handle sentences with double contrasting polarities. For example, the sentiment polarity score of 'Thanks for making this unpleasing party more pleasing' is predicted to be negative because the word 'unpleasing' has a stronger sentiment intensity than the word 'pleasing' according to the general sentiment lexicon.