The purpose of this project is to apply sentiment analysis on a dataset that is given. Our datasets are tweets about Archeage, Hobbit and Iphone6. As feature extractor CNN model and Word2Vec model (Enchanced with GoogleNews); as classifier Evaluation class; to seperate the dataset into multiple files, (our own program) DataSeperator is used.

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In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networksthat has successfully been applied to analyzing visual imagery.

A ConvNet arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a ConvNet transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3.

• word2vec is a group of Deep Learning models developed by Google with.The aim of capturing the context of words while at the same time proposing a very efficient way of preprocessing raw text data.

• This model takes as input a large corpus of documents like tweets or news articles and generates a vector space of typically several hundred dimensions. Each word in the corpus is being assigned a unique vector in the vector space.

• The powerful concept behind word2vec is that word vectors that are close to each other in the vector space represent words that are not only of the same meaning but of the same context as well. Since word2vec relies on Deep Neural Nets to detect patterns, we can rely on it to detect multiple features on different levels of abstractions.

• On a more general level, word2vec embeds non trivial semantic and syntactic relationships between words. This results in preserving a rich context.

• The purpose and usefulness of Word2vec is to group the vectors of similar words together in vectorspace. That is, it detects similarities mathematically.

• Word2vec creates vectors that are distributed numerical representations of word features, features such as the context of individual words.

• It does so without human intervention.

• Works similar with gene2vec, like2vec and follower2vec.

To have more efficient outputs faster (due to the sentence based working principle of Word2vec) , a program is coded by us to split a whole dataset into multiple text files. After the usage of DataSeperator, the output data is manually splitted into two categories which are Train and Test. %80 part of the d ata is set as the Train category and the rest of the data is set as the Test category as the project is prerequired that condition.

1- Dataset is introduced to program and is processed.

2- A custom neural network is created using Word2vec class.

3- Best adjustments are found and applied to the dataset.

4- How many times that Train transaction should be done is found and applied.

5- Output file is saved then implemented into program.

6- Program is used with two words for testing.

1- Used Twitter datasets and word2vec model are defined.

2- Required adjustments are made to create a CNN.

3- Datasets are implemented into program with getDatasetIterator() function as train and test categories.

4- Train transaction is continued using necessary data.

5- A prediction transaction is made to the first negative data in dataset.

6- Datas that compiled by Evaluation class, are seperated into needed categories.