ABSTRAT:
Online social networks (OSN) provide relevant information on users’ opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a Knowledge-Based Recommendation System (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a Convolutional Neural Network (CNN) and a Bi-directional Long Short-Term Memory (BLSTM) – Recurrent Neural Networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.
EXISTING SYSTEM :
The sentiment analysis helps industries to formulate marketing strategies, support after-sale services [29], develop health monitoring system, RS [3], among other services. Sentiment analysis can be performed by: (i) machine learning [30]; (ii) lexicon-based technique using a word-dictionary of textual information or corpus-based approach, in which the polarity value is computed based on the occurrences of the terms in the corpus; (iii) a hybrid technique, which combines machine learning and word-dictionary approaches. The machine learning approach needs a large number of data to obtain reliable results from sentiments; for instance, Chen et al. [31] performs the machine learning approach with a neural network model using BiLSTM-CRF and CNN using 14,492 sentences in the training phase.
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
It is worth noting that, currently, scarce studies about lexicon-based metrics take into account profile parameters. In this research, our proposed sentiment metric, eSM2 complements the ESM by considering the user’ geographic location and the theme of the sentence. B. Recommendation System RS predicts useful items for the user, considering what the user may be interested in. For this prediction, some data are extracted, for example, user’s profile, user’s preferences and past behaviour [37]. There are commonly three RS approaches: content-based, collaborative filtering and hybrid-based. The content-based approach works with the description of an item and the profile of the user’s preference; the suggestion of items is based on what the user already liked. The collaborative filtering analyses the user’s behaviour and preferences and explores similar preferences among people [38]. The hybrid approach combines both methods
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
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
