Absa project
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This document discusses aspect-based sentiment analysis (ABSA) which aims to identify aspects of target entities and the sentiment expressed towards each aspect. It describes 4 tasks: 1) aspect term extraction, 2) aspect term polarity detection, 3) aspect term categorization, and 4) aspect category polarity detection. It provides examples and discusses the tools and methods used for each task, including using the Stanford CoreNLP parser and NLTK. Challenges included some aspect terms not being correctly extracted by rules and assigning polarity when multiple aspects are present. The dataset came from restaurant reviews in XML format.
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Aspect Based Sentiment Analysis
This document discusses aspect based sentiment analysis using recurrent neural networks. It describes annotating review data and developing a GUI for annotation. An API was created to extract aspect terms. A recurrent neural network model was implemented using Deeplearning4j with backpropagation through time to classify inputs. The system was trained on 1/3 of the data and achieved accuracy on the test data. Challenges included not reaching 100% accuracy and most terms being unrelated to aspects. Future work proposed using additional features and different neural network architectures.
Aspect Extraction Performance With Common Pattern of Dependency Relation in ...
Aspect Extraction Performance With Common Pattern of Dependency Relation in ...Nurfadhlina Mohd Sharef
A. S., Shafie, Sharef, N. M., Murad, M. A. A., Azman, A., (2018), "Aspect Extraction Performance With Common Pattern of Dependency Relation in Multi Aspect Sentiment Analysis", 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP18), Kota Kinabalu, in press.Social Media Sentiments Analysis
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This document discusses aspect based sentiment analysis using recurrent neural networks. It describes annotating review data and developing a GUI for annotation. An API was created to extract aspect terms. A recurrent neural network model was implemented using Deeplearning4j with backpropagation through time to classify inputs. The system was trained on 1/3 of the data and achieved accuracy on the test data. Challenges included not reaching 100% accuracy and most terms being unrelated to aspects. Future work proposed using additional features and different neural network architectures.
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This level of error in the original tagging (nearly 50%) likely
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level of noise in the training data, it would be nearly impossible
for any model to learn to correctly identify sarcasm.
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In summary, this project attempted to use NLP techniques to build
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This tutorial provides an introduction to opinion mining. Some of the slides are based on Bing Liu's slides on opinion mining.
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This document provides an overview of sentiment analysis on Twitter. It discusses how sentiment analysis can be used to determine sentiment behind texts and social media updates. The document outlines the methodology used, including data collection from Twitter, preprocessing, feature extraction, and using classifiers like Naive Bayes to predict sentiment. It also discusses applications of sentiment analysis and future areas of improvement, such as using different algorithms and temporal analysis. The goal is to more accurately analyze human sentiment from social media data.
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Based on my review, I do not believe this exchange is
actually sarcastic. It appears to be a genuine discussion of moral
philosophy comparing different crimes.
This level of error in the original tagging (nearly 50%) likely
explains the poor performance of the models. With such a high
level of noise in the training data, it would be nearly impossible
for any model to learn to correctly identify sarcasm.
7. Conclusions and Future Work
In summary, this project attempted to use NLP techniques to build
a model that could identify sarcastic comments. Unfortunately,
the results were significantly poorer than anticipated, even with
relatively large, pre-tagged corpora.
Sentiment Analysis using Twitter Data
Sentiment Analysis helps to find the opinion of the users using the tweets posted in Twitter about the product.
Opinion mining for social media
This document discusses opinion mining for social media. It provides an introduction to opinion mining and sentiment analysis, and discusses some of the challenges involved in performing opinion mining on social media data, including short sentences, incorrect language, and topic divergence. The document then outlines the Arcomem research project, which aims to perform opinion mining on social media to analyze opinions about events over time. It describes the project's entity, topic and opinion extraction workflow and some of the main research directions.
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We have built an online Movie Recommender System which is based on the analysis of users' ratings history to several movies and their demographic information. We used data from Movielens website. Collaborative filtering and matrix factorization techniques have been used for the implementation. The end result is a web application where a user is recommended with top 20 movies.
Codebase: http://goo.gl/nM7RMy
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НАКАЗУЮ:
1. Затвердити протоколи надання медичної допомоги хворим на:
1.1. рак молочної залози (додається);
1.2. базальклітинний рак шкіри (додається);
1.3. меланому шкіри (додається);
1.4. плоскоклітинний рак шкіри (додається);
1.5. саркому кісток (додається);
1.6. саркому м'яких тканин кінцівок та тулуба (додається);
1.7. рак нирки (додається);
1.8 рак передміхурової залози (додається);
1.9 рак сечового міхура (додається);
1.10 рак статевого члена (додається);
1.11 рак яєчка (додається);
1.12 рак жовчного міхура (додається);
1.13 рак периампулярної зони (додається);
1.14 рак печінки (додається);
1.15 рак підшлункової залози (додається);
1.16 рак позапечінкових жовчних ходів (додається);
1.17 рак тонкої кишки (додається);
1.18 рак шлунка (додається);
1.19 рак губи, ротової порожнини, глотки (додається);
1.20 рак верхньощелепної пазухи (додається);
1.21 рак слинних залоз (додається);
1.22 рак щитовидної залози (додається);
1.23 злоякісну мезотеліому плеври (додається);
1.24 злоякісну тімому (додається);
1.25 рак легені (додається);
1.26 рак стравоходу (додається);
1.27 агресивні нехожкінські лімфоми (додається);
1.28 хворобу Ходжкіна (додається);
1.29 високоагресивні неходжкінські лімфоми (додається);
1.30 індолентні нех
sexta parte
This short document promotes the creation of presentations using Haiku Deck on SlideShare. It contains three stock photos with captions and a call to action encouraging the reader to get started creating their own Haiku Deck presentation on SlideShare.
Module 2
The document discusses planning for building a website. It emphasizes that planning is crucial so the website has clear direction and avoids missed deadlines or backtracking. It discusses organizational structures like hierarchy, sequential, and matrix structures. It also discusses content strategies like blogs, videos, and social media links to provide visitors with information. Finally, it outlines steps to design a website, including getting a domain name, choosing a web host, setting a budget, gathering content, designing pages, and testing the site.
RESUME - BIJU
Biju Thayyil Chacko is seeking an Administration Officer position with over 9 years of experience in administration and accounts roles in the Gulf region. He holds a B.A. in Economics from Calicut University in India. His most recent role was as an Administration Executive with Emirates Technical Associates in Dubai where he coordinated HR, administration, and project tasks. Prior to that he held similar administration and office executive roles in the UAE and India, demonstrating experience in areas such as payroll, purchasing, documentation, and customer coordination. He declares that the information in his resume is accurate and looks forward to working sincerely with any new organization.
Matemáticas
This short document promotes creating presentations using Haiku Deck on SlideShare. It encourages the reader to get started making their own Haiku Deck presentation by providing a button to click to begin the process. The document is advertising the creation of presentations on Haiku Deck and SlideShare.
Pramoth_R 2005-2015 (1)
- An experienced banking professional seeking a position in banking and finance that allows applying skills in operations management, business analysis, process management, and relationship management.
- Over 9 years of experience handling end-to-end banking processes including payments, transfers, reconciliations and client management for banks like Citi, Standard Chartered, and United Arab Bank.
- Strong skills in requirements gathering, documentation, testing, and ensuring compliance with regulatory standards while managing teams of up to 21 members.
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Similar to Absa project
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The Newest release of Synaptica V7.1 will include this new Auto Match. The new Term AutoMatch tool will allow for the comparison of terms between two distinct Object Classes (vocabularies) and automatically perform a "mapping" between terms where there is a match based on whole or part of the term descriptions.
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Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
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My presentation slides I used at JavaLand 2024
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Absa project
- 1. Aspect Based Sentiment Analysis Team No: 13
- 2. Overview This task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.
- 3. Phase 1 Task 1 Aspect term Extraction Given a set of sentences with pre- identified entities (e.g., restaurants), identify the aspect terms present in the sentence and return a list containing all the distinct aspect terms. An aspect term names a particular aspect of the target entity. Task 2 Aspect term polarity detection For a given set of aspect terms within a sentence, determine whether the polarity of each aspect term is positive, negative, neutral or conflict (i.e., both positive and negative).
- 4. Phase 2 Task 3 Aspect term categorization Given a predefined set of aspect categories (e.g., price, food), identify the aspect categories discussed in a given sentence. Aspect categories are typically coarser than the aspect terms of Subtask 1, and they do not necessarily occur as terms in the given sentence. Task 4 Aspect category polarity detection Given a set of pre-identified aspect categories (e.g., {food, price}), determine the polarity (positive, negative, neutral or conflict) of each aspect category. Eg: I liked the pizza at the restaurant. The aspect term is pizza and its polarity is positive.
- 5. Dataset Dataset was taken from http://metashare.ilsp.gr:8080/repository/browse/semeval-2014-absa- restaurant-reviews-trial- data/1790ab94464211e388f5842b2b6a04d79bb0323cac4f49939bf5b 99878dc38be/ Restaurant reviews Format: xml
- 6. Tools Used 1. Stanford CoreNLP Parser 2. NLTK
- 7. Task1 1. The reviews are first segmented sentence-wise. 2. We used Stanford CoreNLP parser, to extract dependency relations between words in a sentence, and their Parts Of Speech tag. 3. From the dependency tree, the words are lemmatized. 4. We wrote certain rules based on the relations and POS tags, to extract the aspect terms. 5. A set of 10 rules were written, assuming the reviews are grammatically correct.
- 8. Task 2 1. Took each aspect term from the previous task, and the dependency relations and POS tags, which we got from CoreNLP parser, we got words which would give polarity to the aspect term. 2. SenticNet and SentiWordNet are the resources which we used to find the polarity of the aspect terms given by the words extracted from above step.
- 9. Example. Given a review like, I really liked the pizza at the restaurant but their service was disappointing. In this review the aspect terms are pizza and service. The sentiment conveyed towards pizza is positive whereas for the service its negative. Task 1: Parses the sentence and extracts the words pizza and service as aspect terms. Task 2: The words ‘liked’ and ’disappointed’ conveys the polarity of the aspect terms as positive and negative respectively.
- 10. Task 3 1. Five categories were given for the aspect terms for the data we used Namely, [ food, service, ambience, price, miscellaneous ] 1. In this task, we made use of Wordnet using which we can find for each aspect term which of the 5 categories are most similar to it. 2. First took each aspect term, which was extracted in Task1 and it’s similarity with each one of the 5 categories was calculated using NLTK.Wordnet(). 3. Which category gave highest similarity score with the aspect term, that category was assigned to it.
- 11. Task 4 1. This task was to find polarity of the category. 2. Using the polarities of the aspect terms from Task2, and categories from Task 3, we calculated the polarities of each category. 3. This was done , by taking the average of the polarity of aspect terms belonging to one category.
- 12. Example. Again visiting the previous example. I really liked the pizza at the restaurant but their service was disappointing. The aspect terms extracted were pizza and service. Category{pizza}:: food -> positive Category{service}:: service -> negative
- 13. Attention areas Challenge 1 Aspect term extraction, We used rule based method, and some of the aspect terms weren’t extracted correctly by the rules Challenge 2 Aspect term polarity In sentences with many aspect terms, it was difficult to assign polarity
- 14. Thank you Indranil Mukherjee Bocki Saujanya Satyandra Venkat