Conventional and structure based sentiment analysis: a survey

Sentiment Analysis is a strand of Natural Language Processing that deals with the emotional polarity a given piece of text has. To gain this understanding from just a string of words, we must first consider a suitable way to break down the text to further classify what each part means. This can be done in a plethora of ways, which mostly stem from the understanding of a classifier. We believe that there is a large amount of information stored in structure-based features within the text, for instance, where the writer may place negation-terms not, neither and how this affects the overall polarity. Similarly, how words in sentences or sections, remote to the current-analysed section, may affect the polarity of said section. A combination of features from both a conventional and a structure-based understanding may also provide us with a larger accuracy in polarity. Therefore, this paper aims to explain both conventional sentiment analysis methods with structure-based methods as well as their practices, advantages and disadvantages concluding with how sentiment analysis can move forward with the appropriation of hybrid methods (methods involving motifs, practices and understandings) from conventional and structure-based methods, for classification.