© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods.