## 5 Discussion and Conclusions  
The main contribution of the article is in applying an automatic thematic analysis of concordance lines in a corpus using LSTM. The proposed methodology in this study is useful in various ways. First, the automatic categorization considers the aboutness of the corpus by the relationship between collocates and keywords, as well as their occurrences within the context of the whole text. The contribution this article provides is a deeper automatic thematic analysis than the previous studies, as discussed in the literature review. Linguists such as [PERSON] et al. [2] and [PERSON] and [PERSON] [10] focus only on the analysis of the aboutness of the corpus by quantitively analyzing the collocational network. On the other hand, researchers in the field of computer science, such as [PERSON] et al. [19] and [PERSON] et al. [20], apply classification and sentiment analysis of the whole text without looking at the semantic shifts of keywords in their concordance lines. The proposed method deals with how the collocates are used in their specific context through a thematic analysis of the concordance lines. The categorization of the concordance lines determines which themes are given more concern than the others in the whole corpus. Second, the findings of this paper help to reassert the original result of the manual analysis of the randomly selected lines by looking at some other lines in the automatic categorization. In this study, when we checked some of the automatically categorized lines, we found them in different categories from the ones they had been place in during the original categorization. However, in semantic use, the automatic categorization seems to be more accurate than the original, manual categorization. This automatic thematic analysis method fills the gap proposed by [PERSON] [3] that the collocation analysis does not provide a deep interpretation of the aboutness of a corpus. Thus, this algorithm may help analysts to revisit their manual categorization of some lines. Regarding the specific use of LSTM in thematic analyses, we have seen in the literature that this methodology is used for various purposes, such as sentiment analysis and document classification. Applying the method proposed in the current study will increase the efficiency of the result through a full analysis of the concordance lines, specifically for studies that investigate complex types of themes. The findings of the research suggest the effectiveness of the proposed methodology for the thematic analysis of media coverage. This method is applied to provide a deep thematic analysis. In other words, the current proposed methodology divides the concordance lines into themes in specific orders of discourse and topics (e.g., themes in the UK press coverage for the Syrian chemical attack). The proposed method of this research should take into account the following considerations. First, in applying this method, analysts need to look at the lines of the collocates and categorize a random selection of lines manually to train the algorithm before the automatic categorization. Further, analysts should apply the confusion matrix to test the algorithm before conducting the whole analysis. Then, the incorrect classifications should be reconsidered to improve the algorithm and validate the automatic analysis. For future research, it is recommended that the automatic categorizing should name the themes according to the most salient relationship between collocates/words rather than using the manual naming of themes in the manual analysis of the concordance lines. Conceptualization, Y.A. and M.A. ; methodology, Y.A. and M.A. ; software, M.A. ; validation, Y.A. and M.A. ; formal analysis, Y.A. ; investigation, M.A. ; resources, Y.A. and M.A. ; data curation, M.A. ; writing--original draft preparation, Y.A. and M.A. ; writing--review and editing, Y.A. ; visualization, M.A. ; supervision, Y.A. ; project administration, Y.A. ; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript.