ABSTRACT:

Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence’s orientation (e.g. positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the availability of large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings as weak supervision signals. The framework consists of two steps: (1) learning a high level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; (2) adding a classification layer on top of the embedding layer and use labelled sentences for supervised fine-tuning. We explore two kinds of low level network structure for modelling review sentences, namely, convolutional feature extractors and long short-term memory. To evaluate the proposed framework, we construct a dataset containing 1.1M weakly labelled review sentences and 11,754 labelled review sentences from Amazon. Experimental results show the efficacy of the proposed framework and its superiority over baselines.

EXISTING SYSTEM:

          Lexicon-based methods typically take the tack of first constructing a sentiment lexicon of opinion words (e.g. “wonderful”, “disgusting”), and then design classification rules based on appeared opinion words and prior syntactic knowledge. Despite effectiveness, this kind of methods requires substantial efforts in lexicon construction and rule design. Furthermore, lexicon-based methods cannot well handle implicit opinions, i.e. objective statements such as “I bought the mattress a week ago, and a valley appeared today”. As pointed out in this is also an important form of opinions. Factual information is usually more helpful than subjective feelings. Lexicon-based methods can only deal with implicit opinions in an ad-hoc way.

          DISADVANTAGES:

          Feature engineering also costs a lot of human efforts, and a feature set suitable for one domain may not generate good performance for other domains. This kind of algorithm needs complex lexicon construction and rule design. The existing systems cannot well handle objective statements; it only handles single word based sentiment analysis.

PROPOSED SYSTEM:

          In this work, we propose a novel deep learning framework for review sentence sentiment classification. The framework treats review ratings as weak labels to train deep neural networks. For example, with 5-stars scale we can deem ratings above/below 3-stars as positive/ negative weak labels respectively. The framework generally consists of two steps. In the first step, rather than predicting sentiment labels directly, we try to learn an embedding space (a high level layer in the neural network) which reflects the general sentiment distribution of sentences, from a large number of weakly labelled sentences. That is, we force sentences with the same weak labels to be near each other, while sentences with different weak labels are kept away from one another. To reduce the impact of sentences with rating-inconsistent orientation (hereafter called wrong-labelled sentences), we propose to penalize the relative distances among sentences in the embedding space through a ranking loss. In the second step, a classification layer is added on top of the embedding layer, and we use labelled sentences to fine-tune the deep network. The framework is dubbed Weakly-supervised Deep Embedding (WDE). Regarding network structure, two popular schemes are adopted to learn to extract fixed-length feature vectors from review sentences, namely, convolutional feature extractors and Long Short-Term Memory.

ADVANTAGES:

        The Proposed work leverages the vast amount of weakly labelled review sentences for sentiment analysis. It is much more effective than the previously developed works. The proposed work finds the sentiment not only based on the rating that user gives but also taking into consideration of reviews that they are post, In fact mainly takes an account of review, even though user gave ratings.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10