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Xilisoft Video Joiner for Mac is an advanced software that will allow you to join and merge up to 30 different kinds of formats of video like AVI, MPEG, MP4, WMV, 3GP, H.264, MOV and many more. The class of sentiment in our data frame has to be converted to some number in order to pass it to the model. df_cleaned_train = text_preprocessing(df_train, 'Input') Label encoding Input is the column that contains our text data.Ĭonnection tEmotionffffext_preprocessing may take time dependent on data size. Only the training data needs to be cleaned, not the test and validation data Th.remove_accented_chars() Removes accented characters.Īfter building the text preprocessing function, we need to name it on our data frame. Th.make_base() It takes a sentence and returns the natural sentence. Lambda The function takes a sentence and passes it to the text processing methods. Method progress_apply() It is used when we create a progress bar associated with the method Application(). Tqdm_notebook.pandas() def text_preprocessing(df,col_name):ĭf = df.progress_apply(lambda x:str(x).lower())ĭf = df.progress_apply(lambda x: th.cont_exp(x)) #you're -> you are i'm -> i am df = df.progress_apply(lambda x: th.remove_emails(x))ĭf = df.progress_apply(lambda x: th.remove_html_tags(x))ĭf = df.progress_apply(lambda x: th.remove_special_chars(x))ĭf = df.progress_apply(lambda x: th.remove_accented_chars(x))ĭf = df.progress_apply(lambda x: th.make_base(x)) #ran -> run, Bi-LSTM Networksīi-directional long-term memory (Bi-LSTM) is a neural network architecture in which information is used in both directions forward (past to future) or backward (future to past).įrom tqdm._tqdm_notebook import tqdm_notebook Recurrent neural networks remember the sequence (arrangement) of data and use these data patterns to make predictions. RNN (Recurrent Neural Network) is a type of neural network that is generally used to develop speech and text-related models such as speech recognition and natural language processing models. This article assumes that the reader has basic knowledge about CNN & RNN. In this article, you will be using Bi-Directional LSTM along with word2vec to get better results. There are several ways to detect feelings. In this article, we will focus on emotion detection for text data.