## 2 Literature Review
### Long Short-Term Memory Networks  
Using human resources to handle text classification is a costly process. This problem can be solved by using advanced algorithms to handle the text-classification process. However, using advanced algorithms would require several processes, including data gathering, pre-processing/cleaning, annotation/tagging, and, finally, building a predictive model to facilitate classifying new instances. With the recent increase in the availability of data, this would reduce the time taken by humans to classify data, as well as increase the efficiency of the analysis. The machine learning approach with neural networks, specifically long short-term memory (LSTM), is effective in the field of text classification and natural language processing (NLP). In this research, we adopt LSTM for text classification to perform a thematic analysis using concordance lines that are taken from news articles. We suggest the importance of investigating the concordance lines of the articles rather than only the topics of the article because multiple themes may occur within an article. For example, an article may start with a main idea, but various subthemes may appear clearly in other parts of the article. Thus, we focus on the semantic shift of concepts and ideas in the whole corpus of news articles. Long short-term memory (LSTM) has excelled recently in the field of artificial intelligence and deep learning [17]. It is an advanced method of using the recurrent neural network (RNN). Unlike RNN, which is unable to learn long-term dependencies [18], LSTM keeps track of the input stream for a long period of time. LSTM efficiently improves performance by memorizing the relevant information, and it uses this information for prediction. This process allows for the retention of information passed to neurons, specifically with regard to long sentences [19]. LSTM has been used to perform various classification tasks in NLP. [PERSON] et al. [20] presented document classification tasks using LSTM. The experiment was performed on 20 categories using 20 newsgroup datasets. The accuracy obtained from the classification was approximately 92% [20]. Furthermore, [PERSON] et al. [21] used LSTM for different purposes, such as detecting different types of malware. They looked at software hexadecimal codes instead of dealing with textual data. They classified malware to five different types and obtained an accuracy that reached 67.60%  
In addition, LSTM was used to identify sentence subjectivity using political and ideological datasets. In [PERSON] et al. [22], an experiment was performed to identify each sentence that referenced a specific issue as either as a pro or con. They achieved an accuracy rate approaching 96%. However, these studies did not provide a methodology for a thematic analysis of a specific topic, in contrast to the current study, which investigates themes and topics of the media coverage for the Syrian chemical attack. Other studies applied a general thematic analysis, and the classification was only on a surface level; it was not a deep thematic classification, as is the case in the current study. For example, the dataset of [PERSON] et al. [23] was classified into two classes, either positive or negative, and their model achieved 81.15% accuracy. Further, LSTM has been used in sentiment analysis, reaching 85% accuracy [24]. These studies do not contribute to providing detailed and complex thematic analyses for a corpus around a particular topic. This literature review has shown that previous studies highlight the importance of analyzing keywords and the collocations in a corpus to provide a full understanding of the themes and topics of a corpus. These methodological perspectives are important for providing themes and topics and outlining the semantic structure of a text. The literature suggests that LSTM is an effective tool for classifying the data into groups or themes.