Özet:

The widespread use of social media applications has induced a significant growth of data generation per person. Compared to audio and image-based data, the size of text-based data increases drastically. Besides meaningful words, text-based data may contain contents like punctuation marks (i.e. comma, period, exclamation mark, semicolon, etc.), emojis, URLs, etc. Aforementioned contents are called as noise for text-processing, and they are removed from the dataset at pre-processing phase. Especially in sentiment classification studies on Twitter data sets, data sets are purified from contents such as URLs, punctuations, and emojis at pre-processing phase before word embeddings are created. However, the content called as noise in Twitter social media application, might actually be the part of a user’s sentiment or thought. In this study, we focused on the feature extraction from the noise data that is removed by dataset and the emotion in the tweet was revealed better. We also proposed a novel sentiment classification model aims to use the deep-features extracted by deep learning methods as well as other features extracted manually from the content, which is removed at preprocessing phase of the data. The proposed model was studied on Turkish Twitter dataset. In the experiments, the classification performance of the proposed model based on the same dataset was better than the previous studies.

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