SSGBSAT- a Novel Approach to Perform Sentimental Analysis using Graphology
Shaista Bin-Ti Nazir1, Mohammad Shabaz2
1Shaista Bin-Ti Naziris, M.Tech in Computer Science Engineering from Universal Institute of Engineering & Technology, Lalru, Mohali (Punjab), India.
2Mohammad Shabaz, B.Tech, M.Tech and Pursuing his Ph.D in Computer Science Engineering from Chandigarh University, Mohali (Punjab), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 71-74 | Volume-8 Issue-7, May 2019 | Retrieval Number: F3613048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Handwriting is one of the means to foretell the actions of a person by analyzing the shapes, sizes, altitude, convention and stress of the letters. In a rapid world where people are growing with the new technologies every day, sentimental analysis has also become a key tool to analyze the handwritten data, expressing the behavior or the etiquette of a people. We also have tried to perform analysis on the data of 100 people collected randomly from the college “universal group of institutes”. The challenge was to perform the analysis on handwritten sample of the students and teachers having different thoughts with varying moods. The seven sentiments that needs to be checked are categorized under (Contempt, Anger, Disguise, Joy, Sad, Surprise, and Fear). SSGBSAT is an active or decisive algorithm that takes input images and performs analysis on it by comparing the given input with the stored images of sentiments in the repository to get the authentic results against the above sentiments. The results obtained are stored in table that shows the percentage of each sentiment in given input. Also, total percentage of each sentiment is compared with the total percentage of other sentiments to get the highest sentiment present in the data.
Keyword: Sentimental Analysis, Personality Traits, Handwriting, Graphology.
Scope of the Article: Structural Reliability Analysis.