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Does Fine-tunning Bert Model in multiple times with different dataset make it more accuracy?

i'm totally new in NLP and Bert Model.
What im trying to do right now is Sentiment Analysis on Twitter Trending Hashtag ("neg", "neu", "pos") by using DistilBert Model, but the accurazcy was about 50% ( I tried w Label data taken from Kaggle).
So here is my idea:
(1) First, I will Fine-tunning Distilbertmodel (Model 1) with IMDB dataset,
(2) After that since i've got some data took from Twitter post, i will sentiment analysis them my Model 1 and get Result 2.
(3) Then I will refine-tunning Model 1 with the Result 2 and expecting to have Model (3).
Im not really sure this process has any meaning to make the model more accuracy or not.
Thanks for reading my post.

I'm a little skeptical about your first step. Since the IMDB database is different from your target database, I do not think it will positively affect the outcome of your work. Thus, I would suggest fine-tuning it on a dataset like a tweeter or other social media hashtags; however, if you are only focusing on hashtags and do not care about the text, that might work! My little experience with fine-tuning transformers like BART and BERT shows that the dataset that you are working on should be very similar to your actual data. But in general, you can fine-tune a model with different datasets, and if the datasets are structured for one goal, it can improve the model's accuracy.

If you want to fine-tune a sentiment classification head of BERT for classifying tweets, then I'd recommend a different strategy:
IMDB dataset is a different kind of sentiment - the ratings do not really correspond with short post sentiment, unless you want to focus on tweets regarding movies.
using classifier output as input for further training of that classifier is not really a good approach, because, if the classifier made many mistakes while classifying, these will be reflected in the training, and so the errors will deapen. This is basically creating endogenous labels, which will not really improve your real-world classification.
You should consider other ways of obtaining labelled training data. There are a few good examples for twitter:
Twitter datasets on Kaggle - there are plenty of datasets available containing millions of various tweets. Some of those even contain sentiment labels (usually inferred from emoticons, as these were proven to be more accurate than words in predicting sentiment - for explanation see e.g. Frasincar 2013). So that's probably where you should look.
Stocktwits (if youre interested in financial sentiments)- contain posts that authors can label for sentiments, thus are a perfect way of mining labelled data, if stocks/cryptos is what you're looking for.
Another thing is picking a model that's better for your language, I'd recommend this one. It has been pretrained on 80M tweets, so should provide strong improvements. I believe it even contains a sentiment classification head that you can use.
Roberta Twitter Base
Check out the website for that and guidance for loading the model in your code - it's very easy, just use the following code (this is for sentiment classification):
MODEL = "cardiffnlp/twitter-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
Another benefit of this model is that it has been pretrained from scratch with a vocabulary that contains emojis, meaning it has a deep understanding of them, their typical contexts and co-occurences. This can greatly benefit a social media classification, as many researchers would agree that emojis/emoticons are better predictors of sentiment than normal words.

How to get sentiment tag using word2vec

I'm working on word2vec model in order to analysis a corpus of newspaper.
I have a csv which contains some newspaper like tital, journal, and the content of the article.
I know how to train my model in order to get most similar words and their context.
However, I want to do a sentiment analysis on that. I found some ressources in order to do that but in all the test or train dataframe in the examples, there is already a column sentiment (0 or 1). Do you if it's possible to classify automaticaly texts by sentiment ? I mean put 0 or 1 to each text. I search but i don't find any references about that in the word2vec or doc2vec documentation...
Thanks for advance !

Both Word2Vec & Doc2Vec are just ways to turn words or lists-of-words into 'dense' vectors. Alone, they won't tell you sentiment.
When you have a text and want to deduce which categories it belongs to, that's called 'text classification'. Specifically, if you have just two categories (like 'positive-sentiment' vs 'negative-sentiment', or 'spam' vs 'not-spam'), that's called 'binary classification'.
The output of a Word2Vec or Doc2Vec model might be helpful in that task, but mainly as input to some other chosen 'classifier' algorithm. And, such algorithms require some 'labeled examples' of each kind of text - where you supply the right answer – in order to work. So, you will likely have to go through your corpus of newspaper articles & mark a bunch of them with the answer you want.
You should start by working through some examples that use scikit-learn, the most popular Python library with text-classification tools, even without any Word2Vec or Doc2Vec features, at first. For example, in its docs is an intro:
"Working With Text Data"
Only after you've set up some basic code using generic preprocess/feature-extraction/training/evaluation steps, and reviewed some actual results, should you then consider if adding some features based on Word2Vec or Doc2Vec might help.

Is it possible to predict sentiments using supervised deep learning method?

I created a corpus of 30,000 headlines. I want to predict the sentiments of these headlines using advanced supervised machine learning (deep learning) methods such as RNN, LSTM, or DNN.
My question is: Is it possible to train and test a deep learning model with any labeled datasets such as IMDB movie review, amazon review, or yelp review.
For example, suppose we train and test the IMDB movie reviews dataset with RNN which gives us a 92% f1 score.
Then, can I input my unlabeled dataset (30,000 headlines) and predict their sentiments with this trained and tested model?
The reason for asking this question is that I found many blogs and tutorial with code that uses deep learning methods for sentiment analysis. They use the label dataset and train and test the model and short ....accuracy or f1 score. Nobody goes further and input the unlabeled data and "predict" the sentiment with their model. That is why I am wondering whether it is possible or not.
Thanks for your suggestions and advice.

Good question,
Yes, nothing stops you from testing it against your own dataset. However, this is not how this is supposed to be done:
Consider for example You train a model on Amazon reviews and then you are testing it on Movie reviews. So what's different? The distributions of data are different, this may have a lot of side effects. The choice of words, sentences, metaphors would be different in both the sets of reviews.
For eg. Consider this review in the Life Sciences domain:
"This drug partially cures cancer"
This is most likely to output negative sentiment if you had trained on Amazon Review data because cancer is a negative word in other domains. So, there is a need to train different sentiment classifiers for different domains.
Summary:
Trying to Use Data from the same data source wherever possible.
Train and Predict on the same domain data.

Label reviews in file using sentiwordnet

I'm very new to Sentiment analysis and need some guides. I have a text file of movie reviews and I want to label each review with pos/neg score using sentiwordnet. What steps I should follow to do that?

You should check out NLTK. The package has an interface for sentiwordnet that is simple to use. http://www.nltk.org/howto/sentiwordnet.html
As for the actual sentiment analysis, there are a lot of guides on how to train machine learning models for this task. And sentiwordnet scores can used as features for the classifier.
If you want to use just that alone, the simplest model would be to sum the scores of all the words in the review and make a final judgement.
Check out http://sentiment.christopherpotts.net/ if you want a simple starter to sentiment analysis.
Edit - Some more guides https://marcobonzanini.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn/
http://mlwave.com/movie-review-sentiment-analysis-with-vowpal-wabbit/

scikit-learn: classifying texts using custom labels

I have a large training set of words labeled pos and neg to classify texts. I used TextBlob (according to this tutorial) to classify texts. While it works fairly well, it can be very slow for a large training set (e.g. 8k words).
I would like to try doing this with scikit-learn but I'm not sure where to start. What would the above tutorial look like in scikit-learn? I'd also like the training set to include weights for certain words. Some that should pretty much guarantee that a particular text is classed as "positive" while others guarantee that it's classed as "negative". And lastly, is there a way to imply that certain parts of the analyzed text are more valuable than others?
Any pointers to existing tutorials or docs appreciated!

There is an excellent chapter on this topic in Sebastian Raschka's Python Machine Learning book and the code can be found here: https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb.
He does sentiment analysis (what you are trying to do) on an IMDB dataset. His data is not as clean as yours - from the looks of it - so he needs to do a bit more pre-processing work. Your problem can be solved with these steps:
Create numerical features by vectorizing your text: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html
Train test split: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
Train and test your favourite model, e.g.: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

There are many ways to do this like Tf-Idf (Term frequency - Inverse Document Frequency), Count Vectorizer, Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Word2Vec.
Among all of above mentioned methods, Word2Vec is the best method. You can use a pre-trained model by Google for Word2Vec, available on:
https://github.com/mmihaltz/word2vec-GoogleNews-vectors