  Understanding customer sentiments is of paramount importance in marketing
strategies today. Not only will it give companies an insight as to how
customers perceive their products and/or services, but it will also give them
an idea on how to improve their offers. This paper attempts to understand the
correlation of different variables in customer reviews on a women clothing
e-commerce, and to classify each review whether it recommends the reviewed
product or not and whether it consists of positive, negative, or neutral
sentiment. To achieve these goals, we employed univariate and multivariate
analyses on dataset features except for review titles and review texts, and we
implemented a bidirectional recurrent neural network (RNN) with long-short term
memory unit (LSTM) for recommendation and sentiment classification. Results
have shown that a recommendation is a strong indicator of a positive sentiment
score, and vice-versa. On the other hand, ratings in product reviews are fuzzy
indicators of sentiment scores. We also found out that the bidirectional LSTM
was able to reach an F1-score of 0.88 for recommendation classification, and
0.93 for sentiment classification.
