Questions tagged [sentiment-analysis]

Sentiment analysis refers to categorizing some given data as to what sentiment(s) it expresses. Usually, it refers to extracting sentiment from a text, e.g. tweets or blog posts.

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13 views

Extract audio as text from youtube video?

I am looking to extract the text content of several hundred youtube videos. I have not started this project, but have in mind to use browser automation (e.g. selenium) to download the videos, some ...
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Does GPT-2 has pre trained for sentiment analysis?

I tried sentiment analysis with 345M model of GPT-2. But it took a long time to train. So is there any other GPT-2 model available for sentiment analysis?
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How to make use of POS tags as useful features for a NaiveBayesClassifier for sentiment analysis?

I'm doing sentiment analysis on a twitter dataset (problem link). I have extracted the POS tags from the tweets and created tfidf vectors from the POS tags and used them as a feature (got accuracy of ...
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Unsupervised Sentimental Analysis in R

How would you evaluate unsupervised sentimental analysis? I am reading on evaluating sentimental analysis and learning that much of the classification models that are being used, the data has target/...
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28 views

Build a sentiment model from scratch

I would like to know how I can create a sentiment model from scratch. I have my data, list of texts, with no labels about sentiment. ...
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Embedding layers trained on Amazon Reviews

I am working on research to perform sentiment analysis on Amazon reviews. My data is not labelled so I am now using Lexicon based sentiment analysis such as Vader. I am wondering if it is possible to ...
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56 views

Sentiment analysis of tweets (Train model on a labelled dataset and use on some other unlabelled data)

I have a huge amount of tweets on a particular topic say 'ABC' and the data is not labelled. I want to perform multi-class sentiment analysis of these tweets. I tried many unsupervised clustering ...
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Vader vs TextBlob opposite outcome: why?

I've been studying for a Data Science course and yesterday I was challenged with a sentiment analysis, for which tons of material can be found online. So bear with me, ad I'm trying to get to the ...
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Biasing SVM algorithm towards particular subset of data

I'm training an SVM model for sentiment analysis, based on social media data eg. tweets. The model will be trained using a small selection of a particular company's tweets in order to classify new ...
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25 views

Loaded model predicts well in colab but gives same label and accuracy when downloaded

I have developed a Recurrent Neural Network to perform sentiment analysis on tweets using the Kazanova/sentiment140 dataset in Kaggle. The model looks like this: ...
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Constituency vs Dependency Parsing: What is more effective for Sentiment Analysis?

Parsing is often used to understand the sentiment of complex sentences filled with double negations or very articulated. There are two main ways of parsing a sentence: Constituency and Dependency ...
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Using BM25 to rank words

How effective is it to use BM25 to rank words, to be more specific i have a dictionary of words and i want to rank only words in a document that are also in my dictionary. I want to rank all words in ...
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How to add a new column with labels in a dataframe?

I have thousands of sentences that I would need to label based on their sentiment. An example is ...
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70 views

BPE vs WordPiece Tokenization - when to use / which?

What's the general tradeoff between choosing BPE vs WordPiece Tokenization? When is one preferable to the other? Are there any differences in model performance between the two? I'm looking for a ...
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Sentiment analysis classifier

I would like to build a sentiment classifier from scratch. To do it, I would like to consider to manually assign scores using ngrams. However, I do not know how to assign a score to these ngrams. Do ...
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Sentiment Analysis using FastText

I would need to run some sentiment analysis on some texts. I read about the use as FastText for similar purposes. Since the texts are in Italian I would need that the dictionaries can be in Italian. I ...
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20 views

Text analysis: structure and sentiment

I would need to analyse the structure of texts like this: ...
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19 views

Weighting of words in lexicon based sentiment analysis

I have a a question regarding my current project, i am trying to do a lexicon based sentiment analysis on my data, where i calculate the sentiment score as following: $$ Score = \frac{\sum_{i}{word_i}...
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18 views

How to build a dictionary for sentiment analysis?

I would like to know if it would be possible to build a dictionary using terms/words included in a dataset, in order to be used for sentiment analysis (and build a sentiment analysis classification ...
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What is the input dimension for this keras model?

I'm doing a tutorial, where I have to evaluate the sentiment of IMDB reviews, positive or negative. I first created an index for each word and then replaced every word in each review for each ...
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NLP algorithm: sentiment with specific guidelines

So I have this situation, I have filtered a bunch of single independent sentences that I filtered because they contain the word X (in my case, X = "budget"). if the meaning of the sentence is "budget ...
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Question about balancing training data for sentiment analysis (machine learning)

My question is about when to balance training data for sentiment analysis. Upon evaluating my training dataset, which has 3 labels (good, bad, neutral), I noticed there were twice as many neutral ...
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Degree of Profanity in a Sentence [closed]

Given a comment or a sentence and a list of profane words, How do I write a program to print the degree of profanity in that sentence?
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28 views

Explanation for Why Logistic Regression can be so Accurate in Sentiment Classification?

My question is about how a logistic regression model performs so accurately. In some exploratory experimentation, I compared a logistic regression model against a long short term memory recurrent ...
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38 views

Sentiment Analysis Label Distribution

I am working on Sentiment Analysis model. The dataset I have has three labels POSITIVE , NEGATIVE and NEUTRAL But the problem is the data is not equal for labels. Say out of 100K , 75 K are neutral, ...
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Normalizing impact of outnumbered positive reviews to extract sentiment of each term

I'm trying to extract sentiment of Italian words using reviews that users wrote in Italian amazon. After doing some cleaning (remove punctuation, stop-words, etc.) I used this method to get the ...
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328 views

Using Trainable=True in Keras Embedding obtained better performance

It is suggested by the author of Keras [1] to use Trainable=False when using the embedding layer in Keras to prevent the weights from being updated during training. ...
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423 views

Decision tree in sentiment analysis

Creating a classifier to do "Sentiment Analysis" can be done with several algorithms like SVM, KNN, Neural networks,Decision tree... and lately i have read about Decision tree and how it works and i ...
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Using TF-IDF for feature extraction in Sentiment Analysis

I am working on sentiment analysis for twitter data, for which I have used Vader to get an approximation of sentiment for a tweet. Along with, I have used TF-IDF for feature extraction. These feature ...
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What is purpose of the [CLS] token and why its encoding output is important?

I am reading this article on how to use BERT by Jay Alammar and I understand things up until: For sentence classification, we’re only only interested in BERT’s output for the [CLS] token, so we ...
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Doubt on retrieving tweets for sentiment analysis?

I wanted to do my first project on sentiment analysis. My goal is trying to do sentiment analysis on tweets that mention 'Uber' and see if there is correlation with the stock price: 'UBER'. Problem ...
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219 views

Difference between packaged sentiment analysis tools (TextBlob/NLTK) and training your own classifier?

I'm new to ML and training classifiers in practice, so I was just wondering what the difference was between the built-in sentiment tools of packages such as NLTK and TextBlob as compared to manually ...
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99 views

What is Sentiment Bias? How will it affect a Lexicon Based Sentiment Analysis?

I am comparing deep learning and lexicon/rule-based models for sentiment analysis. When I was doing some research into the limitations of lexicon based models, I came across a journal article that ...
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32 views

How to extract sub sentences from sentence mentioning a particular subject?

I am trying to solve an NLP problem. For a given sentence like : "The Pasta was delicious, the Pizza was average" I want to extract the sentiment attached to food items. Having built my own NER ...
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What does Conv1d do in a sentiment analysis?

I am doing some study on https://www.kaggle.com/anshulrai/cudnnlstm-implementation-93-7-accuracy I understand we need LSTM to capture the sequence of words in the sentience, but I am not quite ...
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Analyzing Sentiments of Financial News related to a Company

I'm trying to build a model which gives me the sentiments of the Financial News related to a company and I want to predict the stock price accordingly. But the major problem that I'm facing is ...
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126 views

Using LSTM for binary text Classification, getting almost same accuracy at each epoch

I am doing Twitter sentiment classification. For that I am using LSTM with pretrained 50d GloVe word embeddings(not training them as of now, might do in future). The tweets are of variable lengths ...
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114 views

Where can I get an untokenized version of GLUE's SST-2 dataset?

On the GLUE faq, they say: Similarly, for SST, the data provided is already tokenized. We're working on obtaining a version that is not tokenized. Feel free to train on other distributions of ...
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Sentiment Analysis: using a dataset (IMDB reviews) to train a neural-net and using it to predict entirely different datasets (Political articles)

We need to analyse a lot of articles relevant to political instability in a given country (things like the possibility of a coalition / a snap election etc). The problem is that I could not find any ...
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How to justify the usage of 200 dimensions in word vectors instead of the 300 dimensions?

When employing machine learning methods in NLP, most of studies use 200 or 300 dimensional vectors. 300 dimensional embeddings carry more information and this, therefore, is considered to produce ...
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List of CNN for Emotion/Sentiment recognition on images with performance on main datasets (IAPS, GAPED, EmoPics, NAPS)

There are more and more databases of pictures classified or rated with emotions. For instance, I know of 4 databases (IAPS, GAPED, EmoPics, NAPS) rating pictures on 2 dimensions: Valance (positive vs ...
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Sentiment Analysis for Q&A based reviews

I'm a self-learning ML enthusiast and I recently started learning NLP and performing Sentiment Analysis on imdb, yelp, amazon datasets(using Python). I came across a dataset where the reviews were in ...
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measuring flip-flop behaviour across several topics

I'm trying to analyze a behavior called "sentiment flipping" of users in a dataset, but I'm not able to step on. Let's suppose that I have two groups of users, say them good and bad users. My ...
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Approach for sentiment analysis of Flemish Twitter data (politics)

I have collected about 280.000 tweets posted by Flemish (Dutch) people concerning the previous elections. I used the twitter API and filtered for mentions of know political parties and politicians. ...
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430 views

Sentiment Analysis of News Headlines

I'm trying to do sentiment analysis of News Headlines about a particular subject mentioned in it. Initially, I used TextBlob library for sentiment analysis to ...
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using dataset to classifying and labelling another unlabeded dataset

I collect a collection of posts from Facebook and I use a published sentiment datset to labeling my collected dataset. is this a right technique and what its name is this transfer-learning ?
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Combining heterogeneous data sets for more powerful machine learning

Suppose we have two data sets of movie reviews; one from IMDB and one from Rotten Tomatoes (RT). Each entry has a written-review and a score attached to it. The concatenated datasets might look like ...
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Testing accuracy has very high accuracy metrics on epoch 1 but decreases rapidly on following epochs

For a binary classification problem my testing accuracy metrics are very high on epoch 1 but it decreases rapidly on further epochs, training trends are similar with epoch 1 having low accuracy ...
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44 views

Emotional tension score in sentences

I am beginner in natural language processing and my goal is to find a way to score sentences based on their emotional tension. More specifically, I would like to know to what degree a sentence ...
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Twitter Dataset

I have found the following dataset, apparently it is the largest tweet dataset: https://www.kaggle.com/kazanova/sentiment140 However, I am looking for a dataset of tweets, with columns containing: ...