This study attempts to elucidate consumer communication in SNS from the perspectives of identity and empathy. Likes, which are reactions that have the meaning of empathy and identity endorsement in SNS, were highlighted, and their facilitating factors were empirically elucidated. This study developed a method to extract posts on specific themes by applying deep learning to actual SNS posts retrieved from Twitter. Furthermore, methods for quantifying text content using LIWC2015 and for extracting and quantifying the meaning of images by applying object detection and topic modeling were developed. These methods were applied to the content analysis of SNS posts. Content analysis of tweets revealed that image posts narrating travel episodes encourage readers to click likes on the posts. The results of this study elucidate that the episodic nature of the posts is one of the factors that encourage likes as an empathy of readers and an endorsement of the identity of the posters in SNS.