Social media has an increasing amount of information that both customers and companies can benefit from. These social media posts can include Tweets or be in the form of vocalization of complements and complaints (e.g., reviews) of a product or service. Researchers have been actively mining this invaluable information source to automatically generate insights. Mining sentiments of customer reviews is an example that has gained momentum due to its potential to gather information that customers are not happy about. Instead of reading millions of reviews, companies prefer sentiment analysis to obtain feedback and to improve their products or services. In this work, we aim to identify information that companies can act on, or other customers can utilize for making their own experience better. This is different from identifying if reviews of a product or service is negative, positive, or neutral. To that end, we classify sentences of a given review as suggestion or not suggestion so that readers of the reviews do not have to go through thousands of reviews but instead can focus on actionable items and applicable suggestions. To identify suggestions within reviews, we employ a hybrid approach that utilizes a recurrent neural network (RNN) along with rule-based features to build a domain-independent suggestion mining model. In this way, a model trained on electronics reviews is used to extract suggestions from hotel reviews.

Written communication lacks the multimodal features such as posture, gesture and gaze that make it easy to model affective states. Especially in social media such as Twitter, due to the space constraints, the sources of information that can be mined are even more limited due to character limitations. These limitations constitute a challenge for understanding short social media posts. In this paper, we present an approach that utilizes multiple binary classifiers that represent different affective categories to model Twitter posts (e.g., tweets). We train domain-independent recurrent neural network models without any outside information such as affect lexicons. We then use these domain independent binary ranking models to evaluate the applicability of such deep learning models on the affect identification task. This approach allows different model architectures and parameter settings for each affect category instead of building one single multi-label classifier. The contributions of this paper are two-folds: we show that modeling tweets with a small training set is possible with the use of RNNs and we also prove that formulating affect identification as a binary classification task is highly effective.