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Recommendation System(RS) is one of machine that uses in many fields of application like music, book, shopping, and etc. With an RS, it makes users easier to find items that are very likely to be searched for. Not only star rating, but testimonials are also one of the data that affects buyers or connoisseurs of a product. The challenge is testimonial is not in numerical data type such as star rating. In this study, the researchers tried to build an architecture to combine the results of the testimonial through sentiment analysis and star rating which are processed separately in an RS. The dataset is reviews of few items in Amazon. The sentiment analysis uses Lexicon-based Approach, which RE use Collaborative filtering with PySpark library. The sentiment analysis has positive, negative, stop words, product-does corpora with double negative or positive words handling, cross negative-positive corpus words handling, and negative of product workless handling. The result is the architecture can be implemented with the testimonial and star rating dataset with giving recommendation items for every user.

Recommendation System, Sentiment Analysis, Collaborative Filtering, PySpark

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[9] Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan, “Large-Scale Parallel Collaborative Filtering for the Netflix Prize”, Algorithmic Aspects in Information and Management, pp. 337-348.
[10] B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2012.
[11] B. Verma, R. Thakur, and S. Jaloree, “Predicting Sentiment from Movie Reviews Using Lexicon Based Model”, International Journal of Computer Sciences and Engineering, Vol. 6, No. 10, pp. 28-34, 2018.

Dea Dania, Lily Wulandari, "Recommendation System for Electronic Product," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.1-6, 2019.

To increased safety and efficiency of road transportation system have promoted automobile manufacturers to integrate wireless communications and networking into vehicles. VANETs have the potential to transform the way people travel through the creation of a safe, interoperable wireless communications network that includes cars, buses, traffic signals, cell phones, and other devices. Due to increasing reliance on communication, computing, and control technologies have become vulnerable to security threats in VANET. To provide the security in VANET by developing the new mechanism which is integrity(data trust), confidentiality, non-repudiation, access control, real-time operational constraints/demands, availability, and data dissemination technique. In our proposed system propose the novel scheme which is called as trust management scheme data dissemination in order to eavesdroppers with threshold based malicious node detection algorithm (TMD) for VANETs that is able to accurately detect and cope with malicious attacks and also evaluate the trustworthiness of both data and mobile nodes in VANETs. At first the data trust is evaluated based on the data sensed and collected from multiple vehicles; after that we evaluate the node trust in two dimensions, i.e., functional trust and recommendation trust. The functional trust is indicating how likely a node can fulfill its functionality. The recommendation trust is indicating how trustworthy the recommendations from a node for other nodes will be, respectively. Finally our experimental result shows our proposed trust management theme is applicable to a wide range of VANET applications to improve traffic safety, mobility, and environmental protection with enhanced trustworthiness.

Vehicular Ad-hoc Networks, Wireless Sensor Networks, Time Division Multiple Access

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[13] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51 – 67, 2016.
[14] A. Alomari, F. Comeau, W. Phillips, and N. Aslam, “New path planning model for mobile anchor-assisted localization in wireless sensor networks,” Wireless Networks, pp. 1–19, 2017.
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[16] Yusuf Perwej “The Hadoop Security in Big Data: A Technological Viewpoint and Analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.3, pp.1-14, 2019.
[17] Rucha Pawar, “Wireless Mesh Network Link Failure Issues and Challenges: A Survey”, IJSRNSC, Vol -6, Issue-3, 2018.

K. Premkumar, R. Baskaran, "The Data Dissemination Resistant Trust Management Scheme for Securing Vehicular Ad Hoc Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.7-13, 2019.

The analysis of social networks is a very challenging research area while a fundamental aspect concerns the detection of user communities. The existing work of emotion recognition on Twitter specifically depends on the use of lexicons and simple classifiers on bag-of words models. The vital question of our observation is whether or not we will enhance their overall performance using machine learning algorithms. The novel algorithm a Profile of Mood States (POMS) represents twelve-dimensional mood state representation using 65 adjectives with combination of Ekman’s and Plutchik’s emotions categories like, anger, depression, fatigue, vigour, tension, confusion, joy, disgust, fear, trust, surprise and anticipation. These emotions classify with the help of text based bag-of-words and LSI algorithms. The contribution work is to apply machine learning algorithm for emotion classification, it takes less time for classification without interfere human labeling. The Multinomial Naïve Bayes classifier works on testing dataset with help of huge amount of training dataset. Measure the performance of POMS & Multinomial Naïve Bayes algorithms on Twitter API. The result shows with the help of Emojis for emotion recognition using tweet contents.

Emotion Recognition, Text Mining, Twitter, Recurrent Neural Networks, Convolutional Neural Networks, Multinomial Naïve Bayes Classifier

[1] NikoColneric and Janez Demsar, “Emotion Recognition on Twitter: Comparative Study and Training a Unison Model” IEEE Transactions on Affective Computing. PP. 1-1.10.1109/TAFFC. 2018. 2807817.
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[4] S. M. Mohammad, X. Zhu, S. Kiritchenko, and J. Martin, “Sentiment, emotion, purpose, and style in electoral tweets,” Information Processing and Management, vol. 51, no. 4, pp. 480–499, 2015.
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[6] X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y.-Y. Wang, “Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval,” Proc. of the 2015 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 912–921, 2015.
[7] O. Irsoy and C. Cardie, “Opinion Mining with Deep Recurrent Neural Networks,” in Proc. of the Conf. on Empirical Methods in Natural Language Processing. ACL, 2014, pp. 720–728.
[8] S. M. Mohammad and S. Kiritchenko, “Using Hashtags to Capture Fine Emotion Categories from Tweets,” Computational Intelligence, vol. 31, no. 2, pp. 301–326, 2015.
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[10] S Kamble, SM Sangve, “Real time Detection of Drifted Twitter Spam Based On Features,” International Journal of General Science and Engineering Research (IJGSER), ISSN 2455-510X,Vol 4(1), 2018,21-23.
[11] Harshad Dattatray Markad, SMS angve, “Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach,” IJSE, Volume 8, Issue 2 July-December 2017.
[12] M. Farhoodi and A. Yari, “Applying machine learning algorithms for automatic Persian text classification,” 2010 6th International Conference on Advanced Information Management and Service (IMS), Seoul, 2010, pp. 318-323.
[13] E. Tromp and M. Pechenizkiy, “Rule-based Emotion Detection on Social Media:” Putting Tweets on Plutchik’s Wheel, arXiv preprint arXiv: 1412.4682, 2014.
[14] S. Chaffar and D. Inkpen, “Using a Heterogeneous Dataset for Emotion Analysis in Text”, in Canadian Conf. on Artificial Intelligence. Springer, 2011, pp. 6267.
[15] S. Aman and S. Szpakowicz, “Identifying Expressions of Emotion in Text, in Int. Conf. on Text, Speech and Dialogue”, vol. 4629. Springer, 2007, pp. 196205.
[16] G. Mishne, “Experiments with Mood Classification in Blog Posts”, in Proc. of ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access, vol. 19, 2005, pp. 321327.

Asharani S Dandoti, Sunil M Sangve, "Emotion Identification between POMS and Multinomial Naive Bayes Algorithm Using Twitter API," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.14-19, 2019.

Speech Recognition has been a wide area of research for a long time now. Researchers have been putting a lot of efforts and devised different methods for the same. For Speech Recognition system, speech signal is divided or segmented into some acoustic units like phonemes, syllables and word which will reduces the search space for unwanted signal or noise. This research work aims at developing an Automatic Speech Segmentation algorithm for Punjabi language which segments the signal into syllabes. For Automatic Speech Syllable Segmentation, a proposed technique detects the syllable boundaries using gamma tone filter and oscillator. In this proposed technique, valley picking picks the valley of the signal and gives the onset of the speech signal. Results of proposed technique was compared with the existing method which takes less time. After that Automatic Speech Classification algorithm classifies the signal into two classes either native or non native. For this, system had been trained using Artificial Neural Network (ANN) for estimating the parameter of Native and Non-Native spekers using Mel Frequency Cepstrum Coefficients (MFCCs) for feature extraction. The whole work was performed in Matlab2016a and the results generated as output with high accuracy.

MFCC, ANN, MATLAB, Punjabi language, gamma tone fiter bank and oscillator

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[2] K. Amino, T. Osanai, “Native vs. non-native accent identification using Japanese spoken telephone numbers,” Speech Communication, vol. 56, no. 1, pp. 70–81, 2014.
[3] M. Wester, C. Mayo, “Accent rating by native and non-native listeners,” in the proceedings of the 2014 ICASSP IEEE International Conference Acoustically Speech Signal Processing, no. i, pp. 7699–7703, 2014.
[4] D. B. Hanchate, M. Nalawade, M. Pawar, V. Pophale, P. K. Maurya,“Vocal Digit Recognition using Artificial Neural Network,” IEEE Journal, vol. 7, no. 10, pp. 88–91, 2010.
[5] L. Bouafif and K. Ouni, “A speech tool software for signal processing applications,” in the proceedings of the 2012 6th International Conference Science Electronics Technology Information Telecommunication SETIT, pp. 788–791, 2012.
[6] E. Sakran, S. M. Abdou, S. E. Hamid, M. Rashwan, “A Review : Automatic Speech Segmentation,” International Journal of Computer Science and Mobile Computing, vol. 6, no. 4, pp. 308–315, 2017.
[7] P. Kumari, D. Shakina Deiv, M. Bhattacharya, “Automatic speech recognition of accented Hindi data,” in the proceedings of the 2014 International Conference on Computation of Power, Energy, Information and Communication(ICCPEIC), pp. 68–76, 2014.
[8] A. Kaur, E. T. Singh, “Segmentation of Continuous Punjabi Speech Signal into Syllables,” World Congress on Engineering and Computer Science, vol. I, pp. 20–23, 2010.
[9] S. P. Panda, A. K. Nayak, “Automatic speech segmentation in syllable centric speech recognition system,” International Journal of Speech Technology, vol. 19, no. 1, pp. 9–18, 2016.
[10] K. Geetha, R. Vadivel, “Syllable Segmentation of Tamil Speech Signals Using Vowel Onset Point and Spectral Transition Measure,” Automatic Control and Computer Sciences, vol. 52, no. 1, pp. 21–25, 2018.
[11] L. Mary, A. P. Antony, “Automatic syllabification of speech signal using short time energy and vowel onset points,” International Journal of Speech Technology, pp. 571– 579, 2018.
[12] S. S. Tirumala, S. R. Shahmiri, A. S. Garhwal, “Speaker Identification feature extraction methods: A Systematic Review”, International Journal of Elsevier, Vol(90),pp. 250-271, 2017.
[13] T. Ozseven, M. Dugenci, “SPeech ACoustic (SPAC): A novel tool for speech feature extraction and classification”, International Journal of Elsevier, 136, pp. 1-8, 2018.
[14] M. R. Gamit, K. Dhameliya, Dr. N. S. Bhatt, “Classification Techniques for Speech Recognition”, International Journal of Emerging Technology and Advanced Engineering, vol. 5, no. 2, pp. 58-63, 2015.
[15] C. P. Bharat, A. A. Desai, “Segmentation of Gujarati words from Continuous spoken Gujarati Speech Signal,” VNSGU Journal of Science and Technology, vol. 4, no. 1, pp. 106-112, 2015.
[16] B. Barhate, D. Sisodiya, R. Deore, “Applications of Speech Recognition: For Programming Languages,” International Journal of Scientific Research in Computer Science and Engineering, vol. 6, no. 1, pp. 6-8, 2018.
[17] Madan, D. Gupta, “Speech Feature Extraction and Classification,” International Journal of Computer Applications, vol. 2, no. 1, pp. 10-15, 2014.
[18] H. K. Soni, “Machine Learning – A New Paradigm of AI,” International Journal Scientific Research in Network Security and Communication, vol. 7, no. 3, pp. 31- 32, 2019.

S. Kaur, M.K. Gill, "Punjabi Speech Syllable Segmentation Using Vowel Onset Point Identification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.20-27, 2019.

Ductal Carcinoma (Breast Cancer) is still the most common type of cancer throughout the world and a frequent cause of cancer death among women. Mammography is the most effective and reliable method for accurate detection of breast cancer in recent years. Micro-calcification (MC) is the tiny specks of calcium which appears in the form of clusters in breast tissue. So the detection of MC cluster in breast tissue plays an important role in enhancing the breast cancer diagnosis. In this report, a knowledge-based approach for the automatic detection and segmentation of micro-calcifications in mammographic images is presented. Segmentation is done by using Adaptive Histogram Equalization (AHE) and by calculating range block and domain block of the image. To validate the efficacy of the suggested scheme, simulation has been carried out using Mammography Image Analysis Society (MIAS) database.

Adaptive Histogram Equalization (AHE), Mammography Image Analysis Society (MIAS), Micro-calcification (MC), Region of interest (ROI)

[1] Motagi, A.C. & Malemath, Virendra. (2018). Detection of Brain Tumor using Expectation Maximization (EM) and Watershed. International Journal of Scientific Research in Computer Science and Engineering. 6. 76-80. 10.26438/ijsrcse/v6i3.7680.
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[6] Alaa Al-Nusirat 1 , Feras Hanandeh 2 , Mohammad Kharabsheh3 , Mahmoud Al-Ayyoub4 and Nahla Al-dhufairi5, ” Dynamic Detection of Software Defects Using Supervised Learning Techniques ”, International Journal of Communication Networks and Information Security (IJCNIS) Vol. 11, No. 1, April 2019.
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[10] Tomasz Arod´z, Marcin Kurdziel , Tadeusz J. Popiela, Erik O.D. Sevre, David A. Yuen, “Detection of clustered microcalcifications in small field digital mammography”, computer methods and programs in biomedicine, Volume 81, Issue 1,Pages 56-65 January 2006.
[11] Arnau Oliver, Albert Torrent, Xavier Llado, Meritxell Tortajada, Lidia Tortajada, Melcior Sentis, Jordi Freixenet, Reyer Zwiggelaar, “Automatic microcalcification and cluster detection for digital and digitised mammograms”, Knowledge-Based Systems, Volume 28, Pages 68-75 , April 2012.
[12] Ait Ibachir I., Es-salhi R., Daoudi I., Tallal S., medromi H., “A survey on Segmentation Techhniques of mammogram Images” in Advances in Ubiquitous Networking 2, Springer, vol 397.,2017.
[13] X. Zhang, X. Li, Y. Feng, “A Medical Image Segmentation Algorithm Based on Bi-directional Region Growing”, Optik Volume 126, Issue 20, Pages 2398-2404, October 2015.
[14] Zhi Luz Gustavo Carneiroy Neeraj Dhungely Andrew P. Bradley,” automated detection of individual micro-calcifications from mammograms using a multi-stage cascade approach” Supported by the Australian Research Council Discovery Project, 2016.
[15] K. Kavitha, N. Kumaravel, "A comparitive study of various microCalcification cluster detection methods in digitized mammograms", IWSSIP and EC-SIPMCS - Proc. 2007 14th Int. Workshop on Systems Signals and Image Processing and 6th EURASIP Conf. Focused on Speech and Image Processing Multimedia Communications and Services, pp. 405-409, 2007.

Pooja Chaudhari, P. B. Bhalerao, "Approach for Segmentation of Micro-calcification in Mammographic Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.28-32, 2019.

Cloud detection is a crucial task and has varied ranges of implications in retrieving important parameters using satellite data. Identifying clouds from clear sky hold great importance in many satellite Imagery applications. Many approaches are used for performing cloud detection on satellite data products. Some of the well-known approaches are a threshold-based approach, a machine learning approach, and a few others, but these approaches lack robustness as these approaches require a profuse amount of time in performing feature-selection. Most of the algorithms fail in taking advantage of spatial arrangement and are time intensive. In tasks like image recognition and object detection, deep learning has outperformed compared to other approaches. In this paper, a deep learning model was proposed for performing cloud detection using INSAT 3D satellite data product which overcomes all the above-mentioned limitations. The proposed model architecture consists of encoder and decoder modules, which will perform sampling, feature extraction, and up-sampling. The proposed model takes five features consisting of SWIR, VIS, TIR1, TIR2, and MIR spectral band’s/channel’s data as input and generates cloud mask as output. Generated cloud mask performs better distinction between cloudy and non-cloudy pixels under different surface conditions, mostly over ice and snow. The proposed model will generate a day-time cloud mask as SWIR and VIS spectral bands data are available only during the day-time.

Deep Learning, Cloud detection, Multispectral channels, Satellite data, INSAT 3D

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[2] Köhler C.; Steiner A.; Saint-Drenan YM.; Ernst D.; Bergmann-Dick A.; Zirkelbach M.; Bouallègue ZB.; Metzinger I.; Ritter B.; “Critical weather situations for renewable energies”–Part B: Low stratus risk for solar power. Renewable energy 2017, 101, 794-803.
[3] Drönner J.; Korfhage N.; Egli S.; Mühling M.; Thies B.; Bendix J.; Freisleben B.; Seeger B. “Fast cloud segmentation using convolutional neural networks”. Remote Sensing 2018, 10, 1782.
[4] Hughes M.; Hayes D.; “Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing”. Remote Sensing, 2014, 6, 4907-26.
[5] Yuan Y.; Hu X.; “Bag-of-words and object-based classification for cloud extraction from satellite imagery”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8, 4197-205.
[6] Bai T.; Li D.; Sun K.; Chen Y.; Li W.; “Cloud detection for high-resolution satellite imagery using machine learning and multi-feature fusion”. Remote Sensing, 2016, 8, 715.
[7] Ishida H.; Oishi Y.; Morita K.; Moriwaki K.; Nakajima TY.; “Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions”. Remote Sensing of Environment, 2018, 205, 390-407.
[8] Mateo-García G.; Gómez-Chova L.; Camps-Valls G.; “Convolutional neural networks for multispectral image cloud masking”. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 2255-2258, IEEE.
[9] Xie F.; Shi M.; Shi Z.; Yin J.; Zhao D.; “Multilevel cloud detection in remote sensing images based on deep learning”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10, 3631-40.
[10] Zhan Y.; Wang J.; Shi J.; Cheng G.; Yao L.; Sun W.; “Distinguishing cloud and snow in satellite images via deep convolutional network”. IEEE Geoscience and Remote Sensing Letters, 2017, 14, 1785-9.

Dhrupa Patel, Sonal Rami, "Deep Learning Technique for Cloud Detection using Satellite Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.33-39, 2019.

In the present generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There are no feasible solution exist to control these problems. In this project, we came up with a framework with which automatic detection of fake profiles is possible and is efficient. This framework uses classification techniques like Support Vector Machine, Naive Bayes and Decision trees to classify the profiles into fake or genuine classes. As, this is an automatic detection method, it can be applied easily by online social networks which has millions of profile whose profiles cannot be examined manually.

Threats, Facebook Immune System, Classification, Training Datasets, Profile Attributes

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R.V. Kotawadekar, A.S. Kamble, S.A. Surve, "Automatic Detection of Fake Profiles in Online Social Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.40-45, 2019.

A simple and efficient method for coloring the gray-scale images into the color images with the existing color map or with creating a new one. The conversion of grayscale images into the color images using different algorithm methods. This color matching technique helps in adding chromatic values to a colorless image and measure for color transfer. Moderately than choosing the entire color from the source to the target image transfer RGB colors from a palette to color gray scale components, by matching difference information between the images. The target image achieved high quality with higher PSNR and lower MSE metrics. It shows that this simple method can be successfully applied to a variety of gray scale images and it can be applicable in different applications.

Gray scale image, RGB image, color map, lockup table, color matching

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P. Ravi, "Performance Analysis of Convert a Gray Image to Color Image," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.46-49, 2019.

Vehicle drivers uses Google Maps (Navigator) during their travelling to get exact road map with proper direction including distance in km and total time remaining to reach out destination. For that people need to have facility like, inbuilt GPS system in vehicle or cell phone with internet connectivity. GPS (Global Positioning System) basically works with satellite in orbit. It is used to determine location of user vehicle. For this user must require having GPS in hand set. Problem with existing navigator is, it is providing shortest path routes to traveller and that creates problem for traveller. Sometimes shortest path with slow speed road creates problem of delay in travelling also. So, solution with speed on road acknowledgement required. For that in this paper algorithm is provided for solving this problem.

Navigator, Search Engine, GPS, Speed required on Road, Direction

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Raina D. Gaharwar, Birajkumar V. Patel, "Navigation Based on Distance and Speed Required on Road," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.50-53, 2019.

In the areas of marketing and electronic advertising, Opinion Mining has a broader domain. The advertiser must analyze the performance / popularity of the advertisements he has posted on the site. The mechanism based on the star rating can be fraudulent, due to robots or automatic responders. Therefore, it is necessary to analyze the current entity system or products using reviews (comments). Opinion Mining refers to the extraction of those lines or phrases in the huge raw data that express an opinion. On the other hand Sentimental Analysis is the analysis of feelings identifies the polarity (sentiment) of the opinion that is extracted from the review. Today, social networking sites and online shopping sites are used by users to express their opinion on products, events, peoples etc. Many users that express their opinion regarding any entity/Product, there may be chances that reviews are not written in correct form (Dictionary). Because of reviews available on these sites may contain noise such as spelling errors, typographical errors, standard abbreviations, and elegant writing. It is necessary to make data noise-free so that it can be used for opinion extraction. This paper describe a framework that was proposed to conduct opinion analysis of noisy reviews using techniques such as calculate similarity of terms and frequency of the document. The reviews of different products have been tested by this framework and the corresponding result is shown in negative (-ve) and positive (+ve ) form. The results are satisfactory for all the tested products.

Opinion mining, Sentiment Analysis, Opinion extraction, Document Frequency

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Kirti Kushwah, Rajendra Kumar Gupta, "A New Approach to Handling Erroneous Reviews in Opinion Mining," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.54-62, 2019.