Aspect Based Sentiment Analysis (ABSA) provides
further insight into the analysis of social media. Understanding
user opinion about different aspects of products, services or
policies can be used for improving and innovating in an effective
way. Thus, it is becoming an increasingly important task in the
Natural Language Processing (NLP) realm. The standard pipeline
of aspect-based sentiment analysis consists of four phases: as-
pect category detection, Opinion Target Extraction (OTE) and
sentiment polarity classification. In this article, we propose an
alternative pipeline: OTE, aspect classification, aspect context
detection and sentiment classification. As it can be observed, the
opinionated words are first detected and then are classified into
aspects. In addition, the opinionated fragment of every aspect is
delimited before performing the sentiment analysis. This paper is
focused on the aspect classification and aspect context detection
phases and proposes a twofold contribution. First, we propose
a hybrid model consisting of a word embeddings model used in
conjunction with semantic similarity measures in order to develop
an aspect classifier module. Second, we extend the context detec-
tion algorithm by Mukherjee et al. to improve its performance.
The system has been evaluated using the SemEval2016 datasets.
The evaluation shows through several experiments that the use of
hybrid techniques that aggregate different sources of information
improves the classification performance.