Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification
Table 2: Code-mixed data distribution.
['[BOLD] Data', '[BOLD] Positive', '[BOLD] Negative', '[BOLD] Total']
Our code-mixed data comprises of two languages, namely English and Bengali. The matrix and embedded languages are not fixed, i.e. in some cases Bengali grammar is used with English words, while in some cases, it’s the other way around. (meaning worse). We collected data from June 15, 2016 to Dec 1, 2016 (duration of 5 months). Reviews were handpicked based on relevance to the topic (movie/film/show). A total of 800 positive and 800 negative snippets were collected. For training purpose, 600 positive and 600 negative snippets were selected using a random function. For testing, the remaining 200 positive and 200 negative snippets were used. The data used for training and test had no snippets in common. Our code-mixed data is relatively quite small due to the fact that it’s extremely tedious and time consuming to collect snippets consisting of solely English and Bengali (Roman script) sentences, as none of the mentioned APIs provide such facility where we can specify this.