Co-occurrence Toolkit

N.B.: Cette section est uniquement disponible en anglais.

This tool, which can be used for a variety of tasks, offers a set of techniques for building and analysing word co-occurrence matrices with up to 5,000 columns.

The matrices to be built can be both symmetric and asymmetric, and they can represent the co-occurrences of the words either within the whole corpus or within a subset of it.

N.B.: In the case of word co-occurrences, the difference between symmetric and asymmetric matrices is that symmetric matrices assume that the order of words does not matter (i.e., they are represented as undirected graphs where the values in a row and a column are the same), while asymmetric matrices take into account the direction of co-occurrence and, for this reason, are represented as directed graph where the values in a row (i.e., successor) and a column (i.e., predecessor) are not necessarily the same.

Whichever tool you are using, the way to export tables and graphs is very simple (see picture below).

After building any co-occurrence matrix, the user is allowed to extract the relevant information by using about fifteen options listed on the left menu (see the above picture).

N.B.:
- all the below pictures have been obtained by analysing the English version of "The Adventures of Pinocchio" (by Carlo Collodi) and its symmetric word co-occurrence matrix.
- all items in the tables are 'lemmas' because a T-LAB lemmatization has been performed on the Pinocchio corpus first.
- whatever matrix you are analysing, it is always possible to check the text segments in which pairs of words co-occur (see picture below).

2 - The ASSOCIATIONS option, in addition to the indexes used by other T-LAB tools (see Associations and Co-Word Analysis), includes the PPMI (i.e., Positive Pointwise Mutual Information), which is a measure of how much more likely two words are to co-occur than by chance, based on their probabilities in a text corpus. It can be used to distinguish between words that are simply co-occurring by chance and words that are semantically related. It can also reduce the effect of high-frequency words that co-occur with many other words by chance. Moreover, unlike other indexes (e.g., Cosine, Dice, Jaccard etc.) its maximum value is not '1' and its upper bound can vary.

3 - The CLUSTER ANALYSIS offers three methods for analysing a word co-occurrence matrix: Hierarchical, K-Means and Louvain.

All the above three methods use vectors which are normalized by the cosine coefficient, and one of them (i.e., the K-Means) performs the clustering on the first 10 dimensions obtained by a SVD (i.e., Singular Value Decomposition) of the normalized word co-occurrence matrix.

To evaluate the quality of clustering results, T-LAB provides the Silhouette scores for each data point.Moreover, when clicking the ‘Q’ button located at the bottom left corner of the screen, the user is allowed to obtain three different quality indices (i.e.: Calinski-Harabasz, Dunn and ICC-rho).

N.B.:
- Depending on the clustering method, the relationships between words within each cluster can be visualized through different types of charts and graphs.
- When performing a hierarchical clustering, the user is allowed to change the number of clusters (i.e., the cluster partition) within a range from 3 to 20.

4 - The RELEVANT WORDS - SVD provides a relevance score for each word, which is computed by summing the square of its first 3 dimensions (i.e., the eigenvectors), each one multiplied by its corresponding singular value, and then by computing the square root of that sum.

This means that the words with the higher scores are the farthest from the point of origin, which is the point where the horizontal axis (x-axis) and the vertical axis (y-axis) intersect. And, for this reason, they are the words that most contribute to organizing semantic polarizations, which can also have emotional connotations.

N.B.: In this case, the SVD is performed on a centered matrix and therefore it is equivalent to PCA (i.e., Principal Component Analysis).

5 - The SEMANTIC DIVERSITY of each word (i.e., its ability to have links with many other words) is measured by means of the entropy index.

N.B.: The average entropy of the word co-occurrence matrix can be used to quantify the 'complexity' of a text, since more complex texts (i.e., texts in which many words cooccur with a variety of other words) tend to have higher entropy than simpler texts (i.e., texts in which many words cooccur with only a few other words and - for that reason - are more predictable). And, since high entropy corresponds to low predictability, it may be also interesting to check which words in a text have higher predictability values (i.e., low entropy).

6 - The TOPIC ANALYSIS of the word co-occurrence matrix uses the same algorithm of the T-LAB Modeling of Emerging Themes tool (i.e., Latent Dirichlet Allocation and the Gibbs Sampling); however, in this case, both the indexes of the matrix (i.e., the 'i' and the 'j') refer to the same words and the values correspond to their co-occurrences. As can be verified, the results of this approach are quite interesting and consistent.

N.B.: In the table below, the words are ordered by their frequency within each topic.

7 - Regarding the five CENTRALITY MEASURES (i.e., Betweenness centrality, Closeness centrality, Eigenvector centrality, Katz centrality and PageRank centrality) we observe that, especially in the case of a symmetric word co-occurrence matrix, they are closely related to each other. Moreover, they usually rank more highly the words with higher occurrence values. The only exception seems to be the Betweenness centrality. In fact, it is possible for a vertex to have high betweenness centrality (i.e., to be able to connect important parts of the network) without having high indegree or high outdegree.

N.B.:
- All definitions of centrality measures, as well as their algorithms, can be easily checked on Wikipedia.
- In T-LAB, all results of centrality measures are normalized to the maximum value. This means that all of the results are between 0 and 1, which makes them easier to compare.

8 - The ASSORTATIVITY COEFFICIENT is a measure of how likely nodes of a certain type are to be connected to other nodes of the same type (i.e., 'similar' in some respects). In the case of T-LAB, the types refer to the results of a previous cluster analysis. Therefore, (a) if- for any 'i' node - the assortativity coefficient is positive and high, then it indicates that the node is strongly connected with other nodes of the same cluster; (b) if - for any 'k' cluster - the average assortativity coefficient is positive and high, then it indicates that the nodes which belong to the cluster are strongly connected with each other; (c) a global average high positive assortativity coefficient indicates that the clustering algorithm has successfully grouped nodes based on their links within the cluster they belong to. This means that nodes within the same cluster are more likely to be connected to each other than nodes from different clusters.

9 - The AVERAGE PATH LENGTH (or average short path), in this case, is defined as the average number of steps along the shortest paths for all possible pairs of nodes of the word co-occurrence matrix.

10 - The CLUSTERING COEFFICIENT deserves special attention. In fact, the 'local' clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together and to pair up with each other (i.e., something like 'The friend of my friend is my friend.'). In other words, the clustering coefficient of a node (i.e., word) quantifies how close its neighbours (i.e., other words) are to being a tightly connected subgroup (i.e., a clique). It is computed as the proportion of the 'actual' connections among its neighbours compared with the number of all its 'possible' connections. Its maximum value is '1', and the average clustering coefficient of all nodes it is also known as transitivity of the network.

N.B.: When a network has a large clustering coefficient and a small average path length it can be considered a 'small world' (see Wikipedia).

11 - The EDGE DENSITY is a measure of how connected the graph is. It is defined as the ratio of the actual number of edges in the graph to the possible number of edges in the graph.
A high edge density indicates that the nodes in the graph are more likely to be connected to each other. This means that there are many paths between any two nodes in the graph. A low edge density indicates that the nodes in the graph are more likely to be disconnected from each other. This means that there are few paths between any two nodes in the graph.

N.B.: It appears that there is a positive correlation between edge density and clustering coefficient. In fact, both measures refer to the connectivity of a graph and can be used to compare the properties of different graphs (i.e., in this case, the properties of different co-occurrence matrices).

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