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1) The document proposes using text-based emotion detection applied to the consumer goods industry to gauge customer responses, monitor advertising campaigns, and guide product development. 2) The proposed approach uses a transformer-based model with emotion-aware preprocessing and domain knowledge insertion to classify emotions into classes with percentages and explanations. It casts emotion classification as span prediction and uses label-correlation losses. 3) The proposed outputs include generating product analytics based on scraped data, explaining a text's emotions using the model, and testing the model's efficacy on user-entered texts.
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Sentiment Analysis for Software EngineeringHow Far Can We G.docx
Sentiment Analysis for Software Engineering:
How Far Can We Go?
Bin Lin
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Fiorella Zampetti
Department of Engineering
University of Sannio
Italy
Gabriele Bavota
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Massimiliano Di Penta
Department of Engineering
University of Sannio
Italy
Michele Lanza
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Rocco Oliveto
STAKE Lab
University of Molise
Italy
ABSTRACT
Sentiment analysis has been applied to various software engineer-
ing (SE) tasks, such as evaluating app reviews or analyzing develop-
ers’ emotions in commit messages. Studies indicate that sentiment
analysis tools provide unreliable results when used out-of-the-box,
since they are not designed to process SE datasets. The silver bul-
let for a successful application of sentiment analysis tools to SE
datasets might be their customization to the specific usage context.
We describe our experience in building a software library recom-
mender exploiting developers’ opinions mined from Stack Overflow.
To reach our goal, we retrained—on a set of 40k manually labeled
sentences/words extracted from Stack Overflow—a state-of-the-art
sentiment analysis tool exploiting deep learning. Despite such an
effort- and time-consuming training process, the results were nega-
tive. We changed our focus and performed a thorough investigation
of the accuracy of commonly used tools to identify the sentiment of
SE related texts. Meanwhile, we also studied the impact of different
datasets on tool performance. Our results should warn the research
community about the strong limitations of current sentiment anal-
ysis tools.
CCS CONCEPTS
• Information systems → Sentiment analysis;
KEYWORDS
sentiment analysis, software engineering, NLP
ACM Reference Format:
Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta, Michele
Lanza, and Rocco Oliveto. 2018. Sentiment Analysis for Software Engineer-
ing: How Far Can We Go?. In ICSE ’18: ICSE ’18: 40th International Conference
on Software Engineering , May 27-June 3, 2018, Gothenburg, Sweden. ACM,
New York, NY, USA, 11 pages. https://doi.org/10.1145/3180155.3180195
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected]
ICSE ’18, May 27-June 3, 2018, Gothenburg, Sweden
© 2018 Copyright held by the owner/author(s). Publication rights licen.
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It provides examples of techniques like lexicon-based, machine learning-based and deep learning-based approaches.
IRJET - Twitter Sentiment Analysis using Machine Learning
This document summarizes a research paper on Twitter sentiment analysis using machine learning. It describes extracting tweets on a topic, cleaning the data, extracting features, building a logistic regression model to classify tweets as positive, negative or neutral sentiment, and validating the model. The goal is to analyze public sentiment from Twitter data, which has applications in marketing, product feedback, and other areas.
AI for sentiment analysis - An Overview.pdf
Sentiment analysis, also referred to as opinion mining, is a method to identify and assess sentiments expressed within a text. The primary purpose is to gauge whether the attitude towards a specific topic, product, or service is positive, negative, or neutral. This process utilizes AI and natural language processing (NLP) to interpret human language and its intricacies, allowing machines to understand and respond to our emotions.
Supervised Approach to Extract Sentiments from Unstructured Text
Supervised Approach to Extract Sentiments from Unstructured TextInternational Journal of Engineering Inventions www.ijeijournal.com
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.IRJET- Conversational Assistant based on Sentiment Analysis
The document proposes a conversational assistant based on sentiment analysis that can analyze a user's emotional state from their conversation input and recent tweets in order to provide counseling and improve responses. The proposed model uses a sequence-to-sequence model for conversation generation, naive bayes classification for sentiment analysis, and the Twitter API to analyze recent tweets in order to monitor a user's emotional changes over time and provide better assistance. The goal is to create a more empathetic chatbot that can understand a user's emotions and provide relevant responses to help those experiencing depression or other mental health issues.
IRJET - Sentiment Analysis for Marketing and Product Review using a Hybrid Ap...
This document presents a hybrid approach for sentiment analysis that combines a lexicon-based technique and a machine learning technique using recurrent neural networks. It aims to analyze sentiments expressed in tweets towards products and services more accurately. The proposed model first cleans tweets collected from Twitter APIs. It then classifies the tweets' sentiment using both a lexicon-based technique using TextBlob and an LSTM-RNN model. The hybrid approach provides not only classification of sentiment but also a score of sentiment strength. This combined approach seeks to gain deeper insights than single techniques alone.
Sentiment Analysis for Software EngineeringHow Far Can We G.docx
Sentiment Analysis for Software Engineering:
How Far Can We Go?
Bin Lin
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Fiorella Zampetti
Department of Engineering
University of Sannio
Italy
Gabriele Bavota
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Massimiliano Di Penta
Department of Engineering
University of Sannio
Italy
Michele Lanza
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Rocco Oliveto
STAKE Lab
University of Molise
Italy
ABSTRACT
Sentiment analysis has been applied to various software engineer-
ing (SE) tasks, such as evaluating app reviews or analyzing develop-
ers’ emotions in commit messages. Studies indicate that sentiment
analysis tools provide unreliable results when used out-of-the-box,
since they are not designed to process SE datasets. The silver bul-
let for a successful application of sentiment analysis tools to SE
datasets might be their customization to the specific usage context.
We describe our experience in building a software library recom-
mender exploiting developers’ opinions mined from Stack Overflow.
To reach our goal, we retrained—on a set of 40k manually labeled
sentences/words extracted from Stack Overflow—a state-of-the-art
sentiment analysis tool exploiting deep learning. Despite such an
effort- and time-consuming training process, the results were nega-
tive. We changed our focus and performed a thorough investigation
of the accuracy of commonly used tools to identify the sentiment of
SE related texts. Meanwhile, we also studied the impact of different
datasets on tool performance. Our results should warn the research
community about the strong limitations of current sentiment anal-
ysis tools.
CCS CONCEPTS
• Information systems → Sentiment analysis;
KEYWORDS
sentiment analysis, software engineering, NLP
ACM Reference Format:
Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta, Michele
Lanza, and Rocco Oliveto. 2018. Sentiment Analysis for Software Engineer-
ing: How Far Can We Go?. In ICSE ’18: ICSE ’18: 40th International Conference
on Software Engineering , May 27-June 3, 2018, Gothenburg, Sweden. ACM,
New York, NY, USA, 11 pages. https://doi.org/10.1145/3180155.3180195
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected]
ICSE ’18, May 27-June 3, 2018, Gothenburg, Sweden
© 2018 Copyright held by the owner/author(s). Publication rights licen.
The Ultimate Prompt Engineering Guide for Generative AI: Get the Most Out of ...
"The Ultimate Prompt Engineering Guide for Generative AI" provides a comprehensive guide to leveraging the power of AI assistants through effective prompt design. It explores fundamental prompt concepts and details strategies for crafting prompts that maximize output quality. Readers learn about iterative refinement, examples, constraints, and advanced techniques like chaining and decomposition. Case studies demonstrate real-world applications in content creation, coding, analysis, and more. Trends in multimodal, automated, and responsible prompting are also examined. This book is a must-read for anyone seeking to optimize generative AI capabilities.
ML
This document presents a study on developing a machine learning model called PyFin-sentiment to derive sentiment from financial tweets. The study collected 10,000 tweets discussing finance and investing that were manually labeled with their market sentiment (positive, negative, neutral). Existing sentiment models trained on other domains struggled on this specialized financial vocabulary. The study then designed, trained and deployed PyFin-sentiment, which outperformed other models when evaluated on tweets. While performing on par with BERT models on other platforms, PyFin-sentiment has a simpler design and lower costs. The study concludes by publishing PyFin-sentiment as an open-source Python library.
IRJET- Interpreting Public Sentiments Variation by using FB-LDA Technique
This document discusses sentiment analysis techniques for classifying tweets based on their positive, negative, or neutral sentiment. It proposes two Latent Dirichlet Allocation (LDA) based models - Foreground and Background LDA (FB-LDA) and Reason Candidate and Background LDA (RCB-LDA) - to analyze sentiment variation in tweets. FB-LDA can filter background topics and extract foreground topics to identify possible explanations for sentiment changes. RCB-LDA can rank reason candidates expressed in tweets to provide sentence-level sentiment explanations. The proposed techniques are intended to classify tweets and evaluate public sentiment variations by extracting possible reasons for those variations.
SPEECH EMOTION RECOGNITION
This document discusses speech emotion recognition using various machine learning classifiers. It begins with an abstract describing speech emotion recognition and its applications. It then discusses the methodology, which includes preprocessing audio files from the RAVDESS dataset, extracting features like MFCCs and chroma, and classifying emotions using classifiers like Catboost, XGB, HistGradient Boosting, and MLP. The methodology achieves 83.33% accuracy in classifying emotions like happy, calm, disgust, and fearful using these classifiers, outperforming a prior method with 81% accuracy.
Estimating the overall sentiment score by inferring modus ponens law
Estimating the overall sentiment score by inferring modus ponens lawInternational Journal of Advance Research and Innovative Ideas in Education
This document proposes a model to estimate overall sentiment score by applying rules of inference from discrete mathematics. It discusses sentiment analysis and related work using techniques like supervised/unsupervised learning. The problem is identifying sentiment components and restricting patterns for feature identification. Most approaches focus on nouns/adjectives but not verbs/adverbs. The model preprocesses product review datasets using NLTK for stemming, parsing and tokenizing. It builds a lexicon dictionary of positive and negative words. The Lexical Pattern Sentiment Analysis algorithm uses both lexicon and pattern mining - it selects sentence patterns, checks for positive/negative words in the lexicon, and calculates an overall sentiment score.Survey of Various Approaches of Emotion Detection Via Multimodal Approach
This document summarizes various approaches for multimodal emotion detection using features extracted from text, audio, and video data. It discusses how combining multiple modalities can provide more comprehensive insight into a user's emotions compared to a single modality. The document reviews related literature on audio-video, text-video, and multimodal emotion classification systems. It also describes approaches for feature extraction from text, such as identifying semantic words and concepts, and from video, including face detection and facial feature extraction. The proposed system aims to predict emotions by combining features from Twitter text and real-time video captured while a user completes a depression questionnaire.
unit-5.pdf
This document discusses techniques for real-time emotion classification of tweets and other social media posts. It describes an architecture for classifying emotions in tweets using machine learning algorithms like naive Bayes classifiers. Feature extraction methods like n-grams and TF-IDF are used to transform text into numerical feature vectors. The document also evaluates methods for generating contextualized lexicons to assess user opinion in social networks, including a polarity-length approach and seeds-based approach. Finally, it covers an explanation system for a technique called ARBITER that forecasts scientific and technical trends by analyzing publications. ARBITER uses ontologies and Bayesian networks to model evidence, hypotheses and probabilistic relationships between variables.
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一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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NLP Hackathon ppt.pptx.pdf
- 1. Sigmoid Hackathon Idea Submission Phase
- 2. —Sherlock Holmes in “A study in Scarlet” “It is a capital mistake to theorize before one has data.”
- 3. Meet the team :Young_Monks Soumyo Bhattacharjee Fourth Year Undergraduate Students Indian Institute of Technology, Kharagpur Shrinivas Khiste Shivam Pandey Vishal Kumar Harsh Sharma
- 4. Breakdown of the Problem and Impact Strategies to tackle the problem statement 01 02 Overview of Problem Statement High Level Approach Details regarding technology stack needed Use Cases / Proposed Final Outlook 03 04 Requisite Technologies End Demonstrable Output Table of contents
- 6. Emotion Detection Emotion Detection , is the synergistic association of emotions also and technology. Emotion detection seeks to extract finer-grained emotions such as happy, sad, angry, and so on, from human languages rather than coarse-grained and general polarity assignments in Sentiment Analysis, in order to provide fine-grained decision-making. Emotions play a very vital role in decisions that people make. To understand these emotions can open new opportunities for any business including the consumer goods industry. Emotions can form the basis to provide recommendations and improve customer experience at every touchpoint, in-turn maximizing revenue for the company.
- 7. Why Text-Based Emotion Detection ?
- 8. $32.95 Billion Expected to Grow at 16.7% CAGR , 2022 - 2030 Global Emotion Detection and Recognition Market Size
- 9. Use Cases: Consumer Goods Industry Monitor effectiveness of Advertising Campaigns User Experience Monitoring Product Development Strategy Products Customer Acquisition and Retention Taking inspiration from both of the Problem Themes of the Hackathon, a interesting domain was taken into consideration : Text Based Emotion Detection applied to the Consumer Goods Industry Gauge Response to New Products
- 10. High Level Approach You can enter a subtitle here if you need it 02
- 11. Overview TRANSFORMER BASED MODEL Domain Knowledge Insertion 1 Emotion Classes Explainable AI Module Output Emotions (with %) Explanation Emotion Aware Preprocessing 1. Span Prediction Loss 2 2. Label Correlation Aware Loss 3 ( process emotion affecting expressions like emojis, deliberate misspellings, abbreviations,etc ) 1. LIME Explainer 4 2. Visualisation of hidden representations
- 12. Salient Features Semantics of emotion labels guide models attention to generate contextualised input representations Acknowledge emotion affecting expressions while preprocessing input 1. Contextualised input representations 2 2. Emotion Aware Preprocessing Casting multi-label emotion classification as span prediction for better representation and performance Model co-existence of multiple emotions and track label-label correlation to penalise incongruous predictions 4. Span Prediction Loss 2 5. Label Correlation Aware Loss 3 Learning domain specific patterns from text and inserting it to the model to aid classification 3. Domain Knowledge Insertion 1 Explain the output of the model to better guide product development strategy 6. Explainable AI 4
- 13. References 1. Ying, W., Xiang, R., & Lu, Q. (2019). Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) (pp. 316–321). Association for Computational Linguistics. 2. Alhuzali, Hassan & Ananiadou, Sophia. (2021). SpanEmo: Casting Multi-label Emotion Classification as Span-prediction. 1573-1584. 10.18653/v1/2021.eacl-main.135. 3. Gaonkar, R., Kwon, H., Bastan, M., Balasubramanian, N., & Chambers, N. (2020). Modeling Label Semantics for Predicting Emotional Reactions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4687–4692). Association for Computational Linguistics. 4. Garreau, D., & Luxburg, U.. (2020). Explaining the Explainer: A First Theoretical Analysis of LIME.
- 15. Backend Frontend
- 17. Generate Insights based on Scraped Data to provide an overview User Entered Text is used to explain the functioning of the Model used in generation of analytics Product Based Analytics Model Efficacy Testing Planned Outputs to demonstrate
- 18. Front-End Website to Generate Analytics —- Select — Generate Product Analytics
- 19. Front-End Website to Test NLPBackend The product A is very well made. I have been using it for 2 months and so far it has exceeded my expectations. It is very reasonable.I am happy with my purchase and would recommend it to others. Submit Test Model Efficacy ! The product A is very well made. I have been using it for 2 months and so far it has exceeded my expectations. It is very reasonable.I am happy with my purchase and would recommend it to others. Top 3 Emotions
- 20. ThankYou!