---
title: Manohar Gowdru Shridhara
published: true
taxonomy:
    category: [phd2024]
    tag: [lm,nlp,hatespeech]
    author: Daniel Hladek
---
# Manohar Gowdru Shridhara

Beginning of the study: 2021

repository: https://git.kemt.fei.tuke.sk/mg240ia

## Disertation Thesis

in 2023/24

Hate Speech Detection

Goals:

- Write a dissertaion thesis
- Publish 2 A-class journal papers

## Minimal Thesis

(preliminary dissertaion and exam in 2022/23)

Goals:

- Provide state-of-the-art overview.
- Formulate dissertation theses (describe scientific contribution of the thesis).
- Prepare to reach the scientific contribution.
- Publish 4 conference papers.

## First year of PhD study

Goals: 

- Provide state-of-the-art overview.
- Read and make notes from at least 100 scientific papers or books.
- Publish at least 2 conference papers.
- Prepare for minimal thesis.

Resources:

- [Hate Speech Project Page](/topics/hatespeech)
- https://hatespeechdata.com/
- [Hate speech detection: Challenges and solutions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701757/)
- [HateBase](https://hatebase.org/) 
- [Resources and benchmark corpora for hate speech detection: a systematic review]
(https://link.springer.com/article/10.1007/s10579-020-09502-8) 

14.7:

Status:

- Pick a feasible dataset and method to start with: kannada dataset, tagging  sentiment for movie reviews. 
- Studied several papers
- started to work on a streamlit demo
 
Open tasks:

- Try to prepare an experiment with the selected dataset. https://git.kemt.fei.tuke.sk/mg240ia/Hate_Speech_IMAYFLY_and_HORSEHERD
- Focus on making a baseline experiment for sentiment classification using classical methods, such as Transformers. 
- For preparing a web application with demo, learn about streamlit. In progress: https://git.kemt.fei.tuke.sk/mg240ia/Hate-Speech-Detector-Streamlit 

Read Papers : 

- https://aclanthology.org/2020.peoples-1.6.pdf 
- https://aclanthology.org/2022.ltedi-1.14/ 
- https://arxiv.org/abs/2108.03867 
- https://arxiv.org/pdf/2112.15417v4.pdf 
- https://arxiv.org/ftp/arxiv/papers/2202/2202.04725.pdf 
- https://github.com/manikandan-ravikiran/DOSA/blob/main/EACL_Final_Paper.pdf 
- https://aclanthology.org/2020.icon-main.13.pdf 
- http://ceur-ws.org/Vol-3159/T6-4.pdf 
- https://www.researchgate.net/publication/353819476_Hope_Speech_detection_in_under-resourced_Kannada_language 
- https://www.researchgate.net/publication/346964457_Creation_of_Corpus_and_analysis_in_Code-Mixed_Kannada-English_Twitter_data_for_Emotion_Prediction 
- https://www.semanticscholar.org/paper/Detecting-stance-in-kannada-social-media-code-mixed-SrinidhiSkanda-Kumar/f651d67211809f2036ac81c27e55d02bd061ed64 
- https://www.academia.edu/81920734/Findings_of_the_Sentiment_Analysis_of_Dravidian_Languages_in_Code_Mixed_Text 
- https://competitions.codalab.org/competitions/30642#learn_the_details 
- https://paperswithcode.com/paper/creation-of-corpus-and-analysis-in-code-mixed 
- https://paperswithcode.com/paper/hope-speech-detection-in-under-resourced#code 

## Meeting 13.6.

- Implemented a Mayfly and Horse Heard Algorithms in Python and Matlab for HS datasets.
- Written a draft of a paper.
- Performed experiments on HS with Word2Vec, FastText, OneHot.

Tasks:

- Implement open tasks from the previous meetings !!!!!!!!
- Share Scripts with GIT and Drafts with Online Word or Docs !!!
- try https://huggingface.co/cardiffnlp/twitter-roberta-base-hate, try to repeat the training and evaluation



## Meeting 24.5.

- shared colab notebook, with on-going implementation of mayfly algorithm for preprocessing in sentiment recognition in a twitter dataset.

Tasks:

- Implement open tasks from the previous meetings !!!
- [ ] Focus on making a baseline experiment for sentiment classification using classcal methods, such as Transformers.
- [x] Consider using pre-trained embeddings. FastText, word2vec, sentence-transformers, Labse, Laser,

Supplemental tasks:

- [x] Fininsh the mayfly implementation


## Meeting 20.5.

- learned about Firefly  / mayfly optimization algorithm.
- read ten papers,
- wrote 1 page abstract about possible system, based od DBN. 


## Meeting 25.4.

- Learned aboud deep learning lifecycle / evaluation, BERT, RoBERTa, GPT
- Tried HF transformers, Spacy, NLTK, word embeddings, sentence transformers.
- Set up a repo with notes: https://git.kemt.fei.tuke.sk/mg240ia

Tasks:

- [ ] Publish experiments into the repository.
- [ ] Prepare a paper for publication in faculty proceedings http://eei.fei.tuke.sk/#!/
- [ ] Send me draft in advance.

Suplemental tasks:

- [x] For presentation of the results, learn about https://wandb.ai/. This can dispplay results (learning curve, etc.)
- [ ] For preparing a web aplication with demo, learn about streamlit.

## Meeting 12.4.

- Created repositories, empty so far.
- Tried to replicate the results from "Emotion and sentiment analysis of tweets using BERT" paper  and "Fine-Tuning BERT Based Approach for Multi-Class Sentiment Analysis on Twitter Emotion Data".
- The experiments are based on BERT (which kind?),  Tweet Emotion Intensity. 
- Prepared colab notebook with experiments.

Tasks:

- [ ] Finish experiments, upload source codes into git, provide a description of the experiments.
- [ ] Try to improve the results - try different kind of BERT - roberta, electra, xl-net. Can "generative models" be used? (gpt, bart, t5). Can "sentence transformers be used" - labse, laser.
- [x] Learn about "Sentence Transformers".
- [ ] Summarize the results in the table, publish the table on git. 
- [-] Use Markdown for formatting. There is "Typora".
- [-] Continue to improve the SCYR paper.
- If you have some conference in mind, tell me.

## Meeting 25.3.22

- Learned about Transformers, BERT, LSTM and RNN.
- Tried HuggingFace transformers library
- Started Google Colab - executing sentiment analysis, hf  transformers pipeline functions.
- prepared datasets: twitter-roberta Datasets. Experiments a re riunnig, no results yet.
- prepared a short note about nlp and neural networks. 
- still working on the SCYR paper

Tasks:

- [-] finish experiments about sentiment and present results.
- [-] create a repository on git.kemt.fei.tuke.sk and upload your experiments, results and notes. Use you student creadentials.
- [-] continue working on "SCYR" review paper, consider publishing it elswhere (the firs version got rejected).
- [-] prepare an outline for another paper with sentiment classification.

## Meeting 10.3.22

- Improvement of the report.
- Installed Transformers and Anaconda

Tasks:

- Try [this model](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) with your own text.
- Learn how Transformers Neural Network Works. Learn how Roberta Model training  works. Learn how BERT model finetuning works. Write a short memo about your findings and papers read on this topic.
- Pick a dataset: 
    - https://huggingface.co/datasets/sentiment140 (english)
    - https://www.clarin.si/repository/xmlui/handle/11356/1054 (multilingua)
    - https://huggingface.co/datasets/tamilmixsentiment (english tamil code switch)
- Grab baseline BERT type model and try to finetune it for sentiment classification.
- For finetuning and evaluation you can use this scrip https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification
- For finetuning you will need to install CUDA and Pytorch. It can work on CPU or NOT.
- If you need GPU, use the school server idoc.fei.tuke.sk or google Colab.
- Continue working on the paper.
- Remind me about the SCYR conference payment.



## Meeting 21.2.22

- Written a report about HS detection (in progress) 

Tasks:

- Repair the report (rewrite copied parts, make the paragrapsh be logically ordered, teoreticaly - formaly  define the HS detection, analyze te datasets in detail - how do they work. what metric do they use).
- Install Hugging Face Transformers and come through a tutorial



## Meeting 31.1.22

- Read some blogs about transformers
- Installed and tied transformers
- Worked on the review paper
- Picked the Twitter Dataset on keggle
- still selecting a method

Open tasks:

- Continue to work on the paper and share the paper with us.
- Prepare som ideas for the common discussion about the project.
- [ ] Try to prepare an experiment with the selected dataset.
- [ ] You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
- [ ] Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
- [x] Get ready to post a paper on the school PhD conference SCYR, deadline is in the middle of February http://scyr.kpi.fei.tuke.sk/.


### Meeting 10.1.22

- Set up a git account https://github.com/ManoGS with script to prepare "twitter" dataset and "english" dataset for HS detection.
- confgured laptop with  (Anaconda)  / PyCharm, pytorch, cuda  gone throug some basic python tutorials.
- Read some blogs how to use kaggle (dataset database).
- tutorials on huggingface transformers - understanding sentiment analysis.

Open tasks:

- [x] Continue to work on the review - with datasets and methods (specified below).
- [x] Read and make notes about transformers, neural language models and finentuning.
- [ ] Pick feasible dataset and method to start with.
- [ ] You can use the school CUDA infrastructre (idoc.fei.tuke.sk).
- [ ] Set up a repository for experiments, use the school git server git.kemt.fei.tuke.sk.
- [ ] Get ready to post a paper on the school PhD conference SCYR, deadline is in the middle of February http://scyr.kpi.fei.tuke.sk/.

#### Meeting 16.12.21

- A report was provided (through Teams).
- Installed Anaconda and started s Transformers tutorial
- Started Dive into python book

Task:

- Report: Create a detailed list of available datasets for HS.
- Report: Create a detailed description of the state of the art approaches for HS detection.
- Practical: Continue with open tasks below. (pick datasetm, perform classification,evaluate the experiment.)


#### Meeting 10.12.21

No report (just draft) was provided so far.

1. Read papers from below and make notes what you have learned fro the papers. For each note make a bibliographic citation. Write down authors of the paper, name paper of the paper, year, publisher and other important information. 
When you find out something, make a reference with a number to that paper.
You can use a bibliografic manager software. Mendeley, Endnote, Jabref.
2. From the papers find out answers to the questions below.
3. Pick a hatespeech dataset. 
4. Pick an approach and Python library for HS classification.
5. Create a [GIT](https://git.kemt.fei.tuke.sk) repository and share your experiment files. Do not commit data files, just links how to download the files.
6. Perform and evaluate experiments.

#### Meeting 10.11.21

#### First tasks

Prepare a report where you will explain:

- what is hate speech detection,
- where and why you can use hate-speech detection,
- what are state-of-the-art methods for hate speech detection,
- how can you evaluate a hate-speech detection system,
- what datasets for hate-speech detection are available,

The report should properly cite scientific bibliographical sources.
Use a bibliography manager software, such as Mendeley.

Create a [VPN connection](https://uvt.tuke.sk/wps/portal/uv/sluzby/vzdialeny-pristup-vpn) to the university network to have access to the scientific databses. Use scientific indexes to discover literature:

- [Scopus](https://www.scopus.com/)  (available from TUKE VPN)
- [Scholar](httyps://scholar.google.com)  

Your review can start with:

- [Hate speech detection: Challenges and solutions](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701757/)
- [HateBase](https://hatebase.org/) 
- [Resources and benchmark corpora for hate speech detection: a systematic review](https://link.springer.com/article/10.1007/s10579-020-09502-8) 

Get to know the Python programming language

- Read [Dive into Python](https://diveintopython3.net/) 
- Install [Anaconda](https://www.anaconda.com/) 
- Try [HuggingFace Transformers library]( https://huggingface.co/transformers/quicktour.html)

